Publications

2024

  • A. Emam, M. Farag, and R. Roscher, “Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness,” IEEE Geoscience and Remote Sensing Letters, 2024.
    [Bibtex] 
    @article{emam2024confident,
title={Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness},
author={Emam, Ahmed and Farag, Mohamed and Roscher, Ribana},
journal={IEEE Geoscience and Remote Sensing Letters},
year={2024},
publisher={IEEE}
}
  • M. Rußwurm, S. Wang, B. Kellenberger, R. Roscher, and D. Tuia, “Meta-learning to address diverse Earth observation problems across resolutions,” Communications Earth & Environment, vol. 5, iss. 1, p. 37, 2024.
    [Bibtex] 
    @article{russwurm2024meta,
title={Meta-learning to address diverse Earth observation problems across resolutions},
author={Ru{\ss}wurm, Marc and Wang, Sherrie and Kellenberger, Benjamin and Roscher, Ribana and Tuia, Devis},
journal={Communications Earth \& Environment},
volume={5},
number={1},
pages={37},
year={2024},
publisher={Nature Publishing Group UK London}
}
  • E. Bolmer, A. Abulaitijiang, J. Kusche, and R. Roscher, “Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry,” Frontiers in Artificial Intelligence, vol. 7, p. 1298283, 2024.
    [Bibtex] 
    @article{bolmer2024estimating,
title={Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry},
author={Bolmer, Eike and Abulaitijiang, Adili and Kusche, J{\"u}rgen and Roscher, Ribana},
journal={Frontiers in Artificial Intelligence},
volume={7},
pages={1298283},
year={2024},
publisher={Frontiers Media SA}
}
  • N. Prakash, R. Roscher, and M. Y. Bader, “A standardized, globally applicable method for detecting spatial patterns at alpine treeline ecotones,” Copernicus Meetings 2024.
    [Bibtex] 
    @techreport{prakash2024standardized,
title={A standardized, globally applicable method for detecting spatial patterns at alpine treeline ecotones},
author={Prakash, Nishtha and Roscher, Ribana and Bader, Maaike Y},
year={2024},
institution={Copernicus Meetings}
}
  • M. Rußwurm, H. Kerner, R. Roscher, C. Pelletier, H. Alemohammad, G. Muhawenayo, G. Tseng, and R. Hänsch, “Machine Learning for Remote Sensing (ML4RS),” in ICLR 2024 Workshops, 2024.
    [Bibtex] 
    @inproceedings{russwurm2024machine,
title={Machine Learning for Remote Sensing (ML4RS)},
author={Ru{\ss}wurm, Marc and Kerner, Hannah and Roscher, Ribana and Pelletier, Charlotte and Alemohammad, Hamed and Muhawenayo, Gedeon and Tseng, Gabriel and H{\"a}nsch, Ronny},
booktitle={ICLR 2024 Workshops},
year={2024}
}

2023

  • [PDF] [DOI] J. Kierdorf, L. V. Junker-Frohn, M. Delaney, M. D. Olave, A. Burkart, H. Jaenicke, O. Muller, U. Rascher, and R. Roscher, “GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER,” Journal of Field Robotics, vol. 40, iss. 2, p. 173–192, 2023.
    [Bibtex] 
    @article{kierdorf2023growliflower,
title={GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER},
author={Kierdorf, Jana and Junker-Frohn, Laura Verena and Delaney, Mike and Olave, Mariele Donoso and Burkart, Andreas and Jaenicke, Hannah and Muller, Onno and Rascher, Uwe and Roscher, Ribana},
journal={Journal of Field Robotics},
volume={40},
number={2},
pages={173--192},
year={2023},
publisher={Wiley Online Library},
url={https://doi.org/10.1002/rob.22122},
doi={10.1002/rob.22122}
}
  • [PDF] [DOI] B. Ekim, T. T. Stomberg, R. Roscher, and M. Schmitt, “MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets],” IEEE Geoscience and Remote Sensing Magazine, vol. 11, iss. 1, p. 103–114, 2023.
    [Bibtex] 
    @article{ekim2023_mapinwild,
title = {{MapInWild}: {A} remote sensing dataset to address the question of what makes nature wild [{Software} and {Data} {Sets}]},
volume = {11},
issn = {2168-6831, 2473-2397, 2373-7468},
shorttitle = {{MapInWild}},
url = {https://ieeexplore.ieee.org/document/10089830/},
doi = {10.1109/MGRS.2022.3226525},
language = {en},
number = {1},
urldate = {2023-04-04},
journal = {IEEE Geoscience and Remote Sensing Magazine},
author = {Ekim, Burak and Stomberg, Timo T. and Roscher, Ribana and Schmitt, Michael},
month = mar,
year = {2023},
keywords = {notion},
pages = {103--114},
file = {Ekim et al. - 2023 - MapInWild A remote sensing dataset to address the.pdf:/home/timo/Documents/zotero/storage/HGR564UV/Ekim et al. - 2023 - MapInWild A remote sensing dataset to address the.pdf:application/pdf},
}
  • [PDF] [DOI] J. Kierdorf and R. Roscher, “Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower,” IEEE Geoscience and Remote Sensing Letters, 2023.
    [Bibtex] 
    @ARTICLE{kierdorf2023reliability,
author={Kierdorf, Jana and Roscher, Ribana},
title={Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower},
journal={IEEE Geoscience and Remote Sensing Letters},
year={2023},
DOI={10.1109/LGRS.2023.3293802},
ISSN={1558-0571}
}
  • M. H. S. Eddin, R. Roscher, and J. Gall, “Location-aware adaptive normalization: A deep learning approach for wildfire danger forecasting,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
    [Bibtex] 
    @article{eddin2023location,
title={Location-aware adaptive normalization: A deep learning approach for wildfire danger forecasting},
author={Eddin, Mohamad Hakam Shams and Roscher, Ribana and Gall, Juergen},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2023},
publisher={IEEE}
}
  • E. Bolmer, A. Abulaitijiang, L. Fenoglio-Marc, J. Kusche, and R. Roscher, “Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data,” in EGU General Assembly Conference Abstracts, 2023, p. EGU–13367.
    [Bibtex] 
    @inproceedings{bolmer2023framework,
title={Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data},
author={Bolmer, Eike and Abulaitijiang, Adili and Fenoglio-Marc, Luciana and Kusche, J{\"u}rgen and Roscher, Ribana},
booktitle={EGU General Assembly Conference Abstracts},
pages={EGU--13367},
year={2023}
}
  • A. Abulaitijiang, E. Bolmer, R. Roscher, J. Kusche, and L. Fenoglio-Marc, “Eddy identification from along-track altimeter data with multi-modal deep learning,” in EGU General Assembly Conference Abstracts, 2023, p. EGU–6818.
    [Bibtex] 
    @inproceedings{abulaitijiang2023eddy,
title={Eddy identification from along-track altimeter data with multi-modal deep learning},
author={Abulaitijiang, Adili and Bolmer, Eike and Roscher, Ribana and Kusche, J{\"u}rgen and Fenoglio-Marc, Luciana},
booktitle={EGU General Assembly Conference Abstracts},
pages={EGU--6818},
year={2023}
}
  • S. Sharma, R. Roscher, M. Riedel, and G. Cavallaro, “Few-Shot Remote Sensing Image Classification with Meta-Learning,” Authorea Preprints, 2023.
    [Bibtex] 
    @article{sharma2023few,
title={Few-Shot Remote Sensing Image Classification with Meta-Learning},
author={Sharma, Surbhi and Roscher, Ribana and Riedel, Morris and Cavallaro, Gabriele},
journal={Authorea Preprints},
year={2023},
publisher={Authorea}
}
  • N. Penzel, J. Kierdorf, R. Roscher, and J. Denzler, “Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances,” in 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023, p. 572–581.
    [Bibtex] 
    @inproceedings{penzel2023analyzing,
title={Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances},
author={Penzel, Niklas and Kierdorf, Jana and Roscher, Ribana and Denzler, Joachim},
booktitle={2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
pages={572--581},
year={2023},
organization={IEEE}
}
  • A. Edrich, A. Yildiz, R. Roscher, and J. Kowalski, “A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning,” , 2023.
    [Bibtex] 
    @article{edrich2023modular,
title={A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning},
author={Edrich, Ann-Kathrin and Yildiz, Anil and Roscher, Ribana and Kowalski, Julia},
year={2023}
}
  • A. Emam, T. T. Stomberg, and R. Roscher, “Leveraging activation maximization and generative adversarial training to recognize and explain patterns in natural areas in satellite imagery,” IEEE Geoscience and Remote Sensing Letters, 2023.
    [Bibtex] 
    @article{emam2023leveraging,
title={Leveraging activation maximization and generative adversarial training to recognize and explain patterns in natural areas in satellite imagery},
author={Emam, Ahmed and Stomberg, Timo T and Roscher, Ribana},
journal={IEEE Geoscience and Remote Sensing Letters},
year={2023},
publisher={IEEE}
}
  • R. Roscher, L. Roth, C. Stachniss, and A. Walter, “Data-Centric Digital Agriculture: A Perspective,” arXiv preprint arXiv:2312.03437, 2023.
    [Bibtex] 
    @article{roscher2023data,
title={Data-Centric Digital Agriculture: A Perspective},
author={Roscher, Ribana and Roth, Lukas and Stachniss, Cyrill and Walter, Achim},
journal={arXiv preprint arXiv:2312.03437},
year={2023}
}
  • L. Drees, D. T. Demie, M. R. Paul, J. Leonhardt, S. J. Seidel, T. F. Döring, and R. Roscher, “Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks,” arXiv preprint arXiv:2312.03443, 2023.
    [Bibtex] 
    @article{drees2023data,
title={Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks},
author={Drees, Lukas and Demie, Dereje T and Paul, Madhuri R and Leonhardt, Johannes and Seidel, Sabine J and D{\"o}ring, Thomas F and Roscher, Ribana},
journal={arXiv preprint arXiv:2312.03443},
year={2023}
}
  • R. Roscher, M. Rußwurm, C. Gevaert, M. Kampffmeyer, J. A. dos Santos, M. Vakalopoulou, R. Hänsch, S. Hansen, K. Nogueira, J. Prexl, and others, “Data-Centric Machine Learning for Geospatial Remote Sensing Data,” arXiv preprint arXiv:2312.05327, 2023.
    [Bibtex] 
    @article{roscher2023data,
title={Data-Centric Machine Learning for Geospatial Remote Sensing Data},
author={Roscher, Ribana and Ru{\ss}wurm, Marc and Gevaert, Caroline and Kampffmeyer, Michael and Santos, Jefersson A dos and Vakalopoulou, Maria and H{\"a}nsch, Ronny and Hansen, Stine and Nogueira, Keiller and Prexl, Jonathan and others},
journal={arXiv preprint arXiv:2312.05327},
year={2023}
}
  • A. Emam, M. M. Ibrahim, and R. Roscher, “Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence,” in Northern Lights Deep Learning Conference Abstracts 2024, 2023.
    [Bibtex] 
    @inproceedings{emam2023confident,
title={Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness in Fennoscandia with Confidence},
author={Emam, Ahmed and Ibrahim, Mohamed Mohamed and Roscher, Ribana},
booktitle={Northern Lights Deep Learning Conference Abstracts 2024},
year={2023}
}
  • M. E. Weber, R. Roscher, P. U. Clark, A. Timmermann, N. R. Golledge, Y. M. Martos, M. Karaesmen, and O. Seki, “Pliocene-Pleistocene ice-ocean-atmosphere dynamics in Iceberg Alley and synchronization with global climate,” AGU23, 2023.
    [Bibtex] 
    @article{weber2023pliocene,
title={Pliocene-Pleistocene ice-ocean-atmosphere dynamics in Iceberg Alley and synchronization with global climate},
author={Weber, Michael E and Roscher, Ribana and Clark, Peter U and Timmermann, Axel and Golledge, Nicholas R and Martos, Yasmina M and Karaesmen, Mehmet and Seki, Osamu},
journal={AGU23},
year={2023},
publisher={AGU}
}
  • J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, and others, “A survey of uncertainty in deep neural networks,” Artificial Intelligence Review, vol. 56, iss. Suppl 1, p. 1513–1589, 2023.
    [Bibtex] 
    @article{gawlikowski2023survey,
title={A survey of uncertainty in deep neural networks},
author={Gawlikowski, Jakob and Tassi, Cedrique Rovile Njieutcheu and Ali, Mohsin and Lee, Jongseok and Humt, Matthias and Feng, Jianxiang and Kruspe, Anna and Triebel, Rudolph and Jung, Peter and Roscher, Ribana and others},
journal={Artificial Intelligence Review},
volume={56},
number={Suppl 1},
pages={1513--1589},
year={2023},
publisher={Springer}
}
  • M. C. Russwurm, R. Roscher, B. A. Kellenberger, S. Wang, and D. Tuia, “Meteor: Meta-learning connecting earth problems observed from space,” in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023.
    [Bibtex] 
    @inproceedings{russwurm2023meteor,
title={Meteor: Meta-learning connecting earth problems observed from space},
author={Russwurm, Marc Conrad and Roscher, Ribana and Kellenberger, Benjamin Alexander and Wang, Sherrie and Tuia, Devis},
booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
year={2023}
}
  • J. Leonhardt, L. Drees, J. Gall, and R. Roscher, “Leveraging Bioclimatic Context for Supervised and Self-Supervised Land Cover Classification,” in German Conference on Pattern Recognition, 2023.
    [Bibtex] 
    @inproceedings{leonhardt2023leveraging,
title={Leveraging Bioclimatic Context for Supervised and Self-Supervised Land Cover Classification},
author={Leonhardt, Johannes and Drees, Lukas and Gall, J{\"u}rgen and Roscher, Ribana},
booktitle={German Conference on Pattern Recognition},
year={2023}
}
  • M. Farag, J. Kierdorf, and R. Roscher, “Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, p. 651–659.
    [Bibtex] 
    @inproceedings{farag2023inductive,
title={Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods},
author={Farag, Mohamed and Kierdorf, Jana and Roscher, Ribana},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={651--659},
year={2023}
}
  • T. T. Stomberg, J. Leonhardt, I. Weber, and R. Roscher, “Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery,” Frontiers in Artificial Intelligence, vol. 6, p. 1278118, 2023.
    [Bibtex] 
    @article{stomberg2023recognizing,
title={Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery},
author={Stomberg, Timo Tjaden and Leonhardt, Johannes and Weber, Immanuel and Roscher, Ribana},
journal={Frontiers in Artificial Intelligence},
volume={6},
pages={1278118},
year={2023},
publisher={Frontiers}
}
  • A. Edrich, A. Yildiz, R. Roscher, and J. Kowalski, “FAIR shallow landslide hazard mapping,” in XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG), 2023.
    [Bibtex] 
    @inproceedings{edrich2023fair,
title={FAIR shallow landslide hazard mapping},
author={Edrich, Ann-Kathrin and Yildiz, Anil and Roscher, Ribana and Kowalski, Julia},
booktitle={XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)},
year={2023},
organization={GFZ German Research Centre for Geosciences}
}

2022

  • [PDF] [DOI] J. Kierdorf, I. Weber, A. Kicherer, L. Zabawa, L. Drees, and R. Roscher, “Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks,” Frontiers in Artificial Intelligence, vol. 5, 2022.
    [Bibtex] 
    @ARTICLE{kierdorf2022behind,
author={Kierdorf, Jana and Weber, Immanuel and Kicherer, Anna and Zabawa, Laura and Drees, Lukas and Roscher, Ribana},
title={Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks},
journal={Frontiers in Artificial Intelligence},
volume={5},
year={2022},
url={https://www.frontiersin.org/article/10.3389/frai.2022.830026},
DOI={10.3389/frai.2022.830026},
ISSN={2624-8212}
}
  • T. T. Stomberg, T. Stone, J. Leonhardt, and R. Roscher, “Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery,” arXiv preprint arXiv:2203.00379, 2022.
    [Bibtex] 
    @article{stomberg2022exploring,
title={Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery},
author={Stomberg, Timo T and Stone, Taylor and Leonhardt, Johannes and Roscher, Ribana},
journal={arXiv preprint arXiv:2203.00379},
year={2022}
}
  • S. Stadtler, C. Betancourt, and R. Roscher, “Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset,” Machine Learning and Knowledge Extraction, vol. 4, iss. 1, p. 150–171, 2022.
    [Bibtex] 
    @article{stadtler2022explainable,
title={Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset},
author={Stadtler, Scarlet and Betancourt, Clara and Roscher, Ribana},
journal={Machine Learning and Knowledge Extraction},
volume={4},
number={1},
pages={150--171},
year={2022},
publisher={MDPI}
}
  • [PDF] C. Betancourt, T. T. Stomberg, A. Edrich, A. Patnala, M. G. Schultz, R. Roscher, J. Kowalski, and S. Stadtler, “Global, high-resolution mapping of tropospheric ozone–explainable machine learning and impact of uncertainties,” Geoscientific Model Development Discussions, p. 1–36, 2022.
    [Bibtex] 
    @article{betancourt2022global,
title={Global, high-resolution mapping of tropospheric ozone--explainable machine learning and impact of uncertainties},
author={Betancourt, Clara and Stomberg, Timo T and Edrich, Ann-Kathrin and Patnala, Ankit and Schultz, Martin G and Roscher, Ribana and Kowalski, Julia and Stadtler, Scarlet},
journal={Geoscientific Model Development Discussions},
pages={1--36},
year={2022},
url={https://gmd.copernicus.org/articles/15/4331/2022/},
publisher={Copernicus GmbH}
}
  • [PDF] [DOI] M. Günder, F. R. Ispizua Yamati, J. Kierdorf, R. Roscher, A. Mahlein, and C. Bauckhage, “Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision,” GigaScience, vol. 11, 2022.
    [Bibtex] 
    @article{gunder2022agricultural,
title={Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision},
author={G{\"u}nder, Maurice and Ispizua Yamati, Facundo R and Kierdorf, Jana and Roscher, Ribana and Mahlein, Anne-Katrin and Bauckhage, Christian},
journal={GigaScience},
volume={11},
year={2022},
publisher={Oxford Academic},
url={ https://doi.org/10.1093/gigascience/giac054},
DOI={10.1093/gigascience/giac054}
}
  • M. Babadi, S. I. Pour, R. Roscher, A. Amiri-Simkooei, and H. Karimi, “Long-term drought monitoring of the Zayandehrud River basin (central Iran) using hydroclimatological models and satellite observations,” Journal of Applied Remote Sensing, vol. 16, iss. 1, p. 14504, 2022.
    [Bibtex] 
    @article{babadi2022long,
title={Long-term drought monitoring of the Zayandehrud River basin (central Iran) using hydroclimatological models and satellite observations},
author={Babadi, Masoud and Pour, Siavash Iran and Roscher, Ribana and Amiri-Simkooei, Alireza and Karimi, Hamed},
journal={Journal of Applied Remote Sensing},
volume={16},
number={1},
pages={014504},
year={2022},
publisher={SPIE}
}
  • D. Marcos, J. Kierdorf, T. Cheeseman, D. Tuia, and R. Roscher, “A Whale’s Tail-Finding the Right Whale in an Uncertain World,” in International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, 2022, p. 297–313.
    [Bibtex] 
    @inproceedings{marcos2022whale,
title={A Whale’s Tail-Finding the Right Whale in an Uncertain World},
author={Marcos, Diego and Kierdorf, Jana and Cheeseman, Ted and Tuia, Devis and Roscher, Ribana},
booktitle={International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers},
pages={297--313},
year={2022},
organization={Springer}
}
  • L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, R. Roscher, and H. Kuhlmann, “Image-based analysis of yield parameters in viticulture,” Biosystems Engineering, vol. 218, p. 94–109, 2022.
    [Bibtex] 
    @article{zabawa2022image,
title={Image-based analysis of yield parameters in viticulture},
author={Zabawa, Laura and Kicherer, Anna and Klingbeil, Lasse and T{\"o}pfer, Reinhard and Roscher, Ribana and Kuhlmann, Heiner},
journal={Biosystems Engineering},
volume={218},
pages={94--109},
year={2022},
publisher={Elsevier}
}
  • [DOI] L. Drees, I. Weber, M. Rußwurm, and R. Roscher, “Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer,” in Proc. of the DAGM German Conference on Pattern Recognition (GCPR), Cham, 2022, p. 495–510.
    [Bibtex] 
    @InProceedings{drees2022time,
author = {Drees, Lukas and Weber, Immanuel and Ru{\ss}wurm, Marc and Roscher, Ribana},
title = {Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer},
booktitle = {Proc. of the DAGM German Conference on Pattern Recognition (GCPR)},
year = {2022},
editor = {Andres, Bj{\"o}rn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldl{\"u}cke, Bastian and Ihrke, Ivo},
pages = {495--510},
address = {Cham},
publisher = {Springer International Publishing},
abstract = {Data imputation of incomplete image sequences is an essential prerequisite for analyzing and monitoring all development stages of plants in precision agriculture. For this purpose, we propose a conditional Wasserstein generative adversarial network TransGrow that combines convolutions for spatial modeling and a transformer for temporal modeling, enabling time-dependent image generation of above-ground plant phenotypes. Thereby, we achieve the following advantages over comparable data imputation approaches: (1) The model is conditioned by an incomplete image sequence of arbitrary length, the input time points, and the requested output time point, allowing multiple growth stages to be generated in a targeted manner; (2) By considering a stochastic component and generating a distribution for each point in time, the uncertainty in plant growth is considered and can be visualized; (3) Besides interpolation, also test-extrapolation can be performed to generate future plant growth stages. Experiments based on two datasets of different complexity levels are presented: Laboratory single plant sequences with Arabidopsis thaliana and agricultural drone image sequences showing crop mixtures. When comparing TransGrow to interpolation in image space, variational, and adversarial autoencoder, it demonstrates significant improvements in image quality, measured by multi-scale structural similarity, peak signal-to-noise ratio, and Fr{\'e}chet inception distance. To our knowledge, TransGrow is the first approach for time- and image-dependent, high-quality generation of plant images based on incomplete sequences.},
doi = {https://doi.org/10.1007/978-3-031-16788-1_30},
file = {:archive_rsg_paper/drees2022gcpr_timeDependentImageGenerationOfPlants.pdf:PDF},
isbn = {978-3-031-16788-1},
}
  • [DOI] J. Leonhardt, L. Drees, P. Jung, and R. Roscher, “Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks,” in Proc. of the DAGM German Conference on Pattern Recognition (GCPR), Cham, 2022, p. 479–494.
    [Bibtex] 
    @InProceedings{leonhardt2022probabilistic,
author = {Leonhardt, Johannes and Drees, Lukas and Jung, Peter and Roscher, Ribana},
title = {Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks},
booktitle = {Proc. of the DAGM German Conference on Pattern Recognition (GCPR)},
year = {2022},
editor = {Andres, Bj{\"o}rn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldl{\"u}cke, Bastian and Ihrke, Ivo},
pages = {479--494},
address = {Cham},
publisher = {Springer International Publishing},
abstract = {Biomass is an important variable for our understanding of the terrestrial carbon cycle, facilitating the need for satellite-based global and continuous monitoring. However, current machine learning methods used to map biomass can often not model the complex relationship between biomass and satellite observations or cannot account for the estimation's uncertainty. In this work, we exploit the stochastic properties of Conditional Generative Adversarial Networks for quantifying aleatoric uncertainty. Furthermore, we use generator Snapshot Ensembles in the context of epistemic uncertainty and show that unlabeled data can easily be incorporated into the training process. The methodology is tested on a newly presented dataset for satellite-based estimation of biomass from multispectral and radar imagery, using lidar-derived maps as reference data. The experiments show that the final network ensemble captures the dataset's probabilistic characteristics, delivering accurate estimates and well-calibrated uncertainties.},
doi = {https://doi.org/10.1007/978-3-031-16788-1_29},
file = {:archive_rsg_paper/leonhardt2022gcpr_probabilisticBiomassEstimation.pdf:PDF},
isbn = {978-3-031-16788-1},
}
  • [PDF] M. Miranda, L. Drees, and R. Roscher, “Controlled Multi-modal Image Generation for Plant Growth Modeling,” in Proc. of the International Conference on Pattern Recognition (ICPR), 2022.
    [Bibtex] 
    @InProceedings{miranda2022controlled,
author = {Miranda, Miro and Drees, Lukas and Roscher, Ribana},
title = {Controlled Multi-modal Image Generation for Plant Growth Modeling},
booktitle = {Proc. of the International Conference on Pattern Recognition (ICPR)},
year = {2022},
file = {:archive_rsg_paper/miranda2022controlled.pdf:PDF},
}
  • I. Obadic, R. Roscher, D. A. B. Oliveira, and X. X. Zhu, “Exploring self-attention for crop-type classification explainability,” arXiv preprint arXiv:2210.13167, 2022.
    [Bibtex] 
    @article{obadic2022exploring,
title={Exploring self-attention for crop-type classification explainability},
author={Obadic, Ivica and Roscher, Ribana and Oliveira, Dario Augusto Borges and Zhu, Xiao Xiang},
journal={arXiv preprint arXiv:2210.13167},
year={2022}
}

2021

  • D. Tuia, R. Roscher, J. D. Wegner, N. Jacobs, X. X. Zhu, and G. Camps-Valls, “Towards a Collective Agenda on AI for Earth Science Data Analysis,” IEEE Geoscience and Remote Sensing Magazine, 2021.
    [Bibtex] 
    @Article{Tuia2021agenda,
author = {Tuia, Devis and Roscher, Ribana and Wegner, Jan Dirk and Jacobs, Nathan and Zhu, Xiao Xiang and Camps-Valls, Gustau},
journal = {IEEE Geoscience and Remote Sensing Magazine},
title = {Towards a Collective Agenda on AI for Earth Science Data Analysis},
year = {2021},
}
  • R. Roscher, “Deep self-taught learning in remote sensing,” in Deep Learning for the Earth Sciences – A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences, G. Camps-Valls, D. Tuia, X. X. Zhu, and M. Reichstein, Eds., Wiley&Sons, 2021.
    [Bibtex] 
    @InCollection{Roscher2021stl,
author = {Ribana Roscher},
title = {Deep self-taught learning in remote sensing},
booktitle = {Deep Learning for the Earth Sciences -- A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences},
publisher = {Wiley\&Sons},
year = {2021},
editor = {Gustau Camps-Valls and Devis Tuia and Xiao Xiang Zhu and Markus Reichstein},
}
  • [PDF] [DOI] C. Betancourt, T. Stomberg, S. Stadtler, R. Roscher, and M. G. Schultz, “AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics,” Earth System Science Data Discussions, vol. 2021, p. 1–26, 2021.
    [Bibtex] 
    @Article{Betancourt2021bench,
author = {Betancourt, Clara and Stomberg, Timo and Stadtler, Scarlet and Roscher, Ribana and Schultz, Martin G.},
journal = {Earth System Science Data Discussions},
title = {AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics},
year = {2021},
pages = {1--26},
volume = {2021},
doi = {10.5194/essd-13-3013-2021},
url = {https://essd.copernicus.org/articles/13/3013/2021/},
}
  • I. Weber, J. Bongartz, and R. Roscher, “Artificial and beneficial–Exploiting artificial images for aerial vehicle detection,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, p. 158–170, 2021.
    [Bibtex] 
    @article{weber2021artificial,
title={Artificial and beneficial--Exploiting artificial images for aerial vehicle detection},
author={Weber, Immanuel and Bongartz, Jens and Roscher, Ribana},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={175},
pages={158--170},
year={2021},
publisher={Elsevier}
}
  • [PDF] [DOI] T. Stomberg, I. Weber, M. Schmitt, and R. Roscher, “jUngle-Net: Using Explainable Machine Learning to Gain New Insights into the Appearence of Wilderness in Satellite Imagery,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, p. 317–324, 2021.
    [Bibtex] 
    @article{stomberg2021jungle,
title={jUngle-Net: Using Explainable Machine Learning to Gain New Insights into the Appearence of Wilderness in Satellite Imagery},
author={Stomberg, T and Weber, I and Schmitt, M and Roscher, R},
journal={ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
volume={3},
pages={317--324},
year={2021},
publisher={Copernicus GmbH},
doi={10.5194/isprs-annals-V-3-2021-317-2021},
url={https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/317/2021/},
}
  • I. Weber, J. Bongartz, and R. Roscher, “ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for improved aerial Vehicle Detection,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021.
    [Bibtex] 
    @InProceedings{weber2021artifive,
author = {Weber, Immanuel and Bongartz, Jens and Roscher, Ribana},
title = {ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for improved aerial Vehicle Detection},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium ({IGARSS})},
year = {2021},
}
  • J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, and others, “A Survey of Uncertainty in Deep Neural Networks,” arXiv preprint arXiv:2107.03342, 2021.
    [Bibtex] 
    @Article{gawlikowski2021survey,
author = {Gawlikowski, Jakob and Tassi, Cedrique Rovile Njieutcheu and Ali, Mohsin and Lee, Jongseok and Humt, Matthias and Feng, Jianxiang and Kruspe, Anna and Triebel, Rudolph and Jung, Peter and Roscher, Ribana and others},
title = {A Survey of Uncertainty in Deep Neural Networks},
journal = {arXiv preprint arXiv:2107.03342},
year = {2021},
}
  • S. Stadtler, J. Kowalski, M. Abel, R. Roscher, S. Crewell, B. Gräler, S. Kollet, and M. Schultz, “KI: STE Project-AI Strategy for Earth System Data,” in Proc. of the EGU General Assembly Conference Abstracts, 2021, p. EGU21–211.
    [Bibtex] 
    @InProceedings{stadtler2021ki,
author = {Stadtler, Scarlet and Kowalski, Julia and Abel, Markus and Roscher, Ribana and Crewell, Susanne and Gr{\"a}ler, Benedikt and Kollet, Stefan and Schultz, Martin},
title = {KI: STE Project-AI Strategy for Earth System Data},
booktitle = {Proc. of the EGU General Assembly Conference Abstracts},
year = {2021},
pages = {EGU21--211},
}
  • C. Betancourt, J. Kowalski, A. Patnala, S. Stadtler, M. G. Schultz, T. Stomberg, R. Roscher, and A. Edrich, “Global fine resolution mapping of ozone metrics through explainable machine learning,” Jülich Supercomputing Center 2021.
    [Bibtex] 
    @TechReport{betancourt2021global,
author = {Betancourt, Clara and Kowalski, Julia and Patnala, Ankit and Stadtler, Scarlet and Schultz, Martin G and Stomberg, Timo and Roscher, Ribana and Edrich, Ann-Kathrin},
title = {Global fine resolution mapping of ozone metrics through explainable machine learning},
institution = {J{\"u}lich Supercomputing Center},
year = {2021},
}
  • [DOI] L. Drees, L. V. Junker-Frohn, J. Kierdorf, and R. Roscher, “Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks,” Computers and Electronics in Agriculture, vol. 190, p. 106415, 2021.
    [Bibtex] 
    @Article{drees2021temporal,
author = {Lukas Drees and Laura Verena Junker-Frohn and Jana Kierdorf and Ribana Roscher},
title = {Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks},
journal = {Computers and Electronics in Agriculture},
year = {2021},
volume = {190},
pages = {106415},
issn = {0168-1699},
doi = {10.1016/j.compag.2021.106415},
keywords = {Generative adversarial networks, Agriculture, Cauliflower, Prediction, Plant growth},
}

2020

  • [PDF] R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke, “Explainable Machine Learning for Scientific Insights and Discoveries,” IEEE Access, vol. 8, iss. 1, pp. 42200-42216, 2020.
    [Bibtex] 
    @Article{Roscher2020explainable,
Title = {Explainable Machine Learning for Scientific Insights and Discoveries},
Author = {Roscher, Ribana and Bohn, Bastian and Duarte, Marco F. and Garcke, Jochen},
Journal = {IEEE Access},
Year = {2020},
Number = {1},
Pages = {42200-42216},
Volume = {8},
Date-added = {2019-05-26 21:15:07 +0200},
Date-modified = {2019-05-26 21:22:58 +0200}
}
  • [PDF] [DOI] J. Kierdorf, J. Garcke, J. Behley, T. Cheeseman, and R. Roscher, “What Identifies a Whale by its Fluke? On the Benefit of Interpretable Machine Learning for Whale Identification,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-2-2020, p. 1005–1012, 2020.
    [Bibtex] 
    @Article{kierdorf2020whale,
AUTHOR = {Kierdorf, J. and Garcke, J. and Behley, J. and Cheeseman, T. and Roscher, R.},
TITLE = {What Identifies a Whale by its Fluke? On the Benefit of Interpretable Machine Learning for Whale Identification},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-2-2020},
YEAR = {2020},
PAGES = {1005--1012},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/1005/2020/},
DOI = {10.5194/isprs-annals-V-2-2020-1005-2020},
Owner = {ribana},
Timestamp = {2020.05.06}
}
  • [PDF] [DOI] R. Roscher, B. Bohn, M. Duarte, and J. Garcke, “Explain it to me – Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable Machine Learning,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-3-2020, p. 817–824, 2020.
    [Bibtex] 
    @Article{roscher2020explain,
AUTHOR = {Roscher, Ribana and Bohn, Bastian and Duarte, Marco and Garcke, Jochen},
TITLE = {Explain it to me - Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable Machine Learning},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-3-2020},
YEAR = {2020},
PAGES = {817--824},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/817/2020/},
DOI = {10.5194/isprs-annals-V-3-2020-817-2020},
Owner = {ribana},
Timestamp = {2020.05.06}
}
  • [PDF] [DOI] R. Roscher, M. Volpi, C. Mallet, L. Drees, and J. D. Wegner, “SemCity Toulouse: A Benchmark for Building Instance Segmentation in Satellite Images,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-5-2020, p. 109–116, 2020.
    [Bibtex] 
    @Article{roscher2020semcity,
AUTHOR = {Roscher, Ribana and Volpi, Michele and Mallet, Clément and Drees, Lukas and Wegner, Jan Dirk},
TITLE = {SemCity Toulouse: A Benchmark for Building Instance Segmentation in Satellite Images},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-5-2020},
YEAR = {2020},
PAGES = {109--116},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-5-2020/109/2020/},
DOI = {10.5194/isprs-annals-V-5-2020-109-2020},
Owner = {ribana},
Timestamp = {2020.05.06}
}
  • [PDF] [DOI] I. Weber, J. Bongartz, and R. Roscher, “Learning with Real-World and Artificial Data for Improved Vehicle Detection in Aerial Imagery,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-2-2020, p. 917–924, 2020.
    [Bibtex] 
    @Article{weber2020real,
AUTHOR = {Weber, I. and Bongartz, J. and Roscher, R.},
TITLE = {Learning with Real-World and Artificial Data for Improved Vehicle Detection in Aerial Imagery},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-2-2020},
YEAR = {2020},
PAGES = {917--924},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/917/2020/},
DOI = {10.5194/isprs-annals-V-2-2020-917-2020},
Owner = {ribana},
Timestamp = {2020.05.06}
}
  • [PDF] [DOI] L. Drees, J. Kusche, and R. Roscher, “Multi-Modal Deep Learning with Sentinel-3 Observations for the Detection of Oceanic Internal Waves,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-2-2020, p. 813–820, 2020.
    [Bibtex] 
    @Article{drees2020multimodal,
AUTHOR = {Drees, Lukas and Kusche, Jürgen and Roscher, Ribana},
TITLE = {Multi-Modal Deep Learning with Sentinel-3 Observations for the Detection of Oceanic Internal Waves},
JOURNAL = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-2-2020},
YEAR = {2020},
PAGES = {813--820},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/813/2020/},
DOI = {10.5194/isprs-annals-V-2-2020-813-2020},
Owner = {ribana},
Timestamp = {2020.05.06}
}
  • L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, H. Kuhlmann, and R. Roscher, “Counting of grapevine berries in images via semantic segmentation using convolutional neural networks,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, p. 73–83, 2020.
    [Bibtex] 
    @Article{zabawa2020counting,
Title = {Counting of grapevine berries in images via semantic segmentation using convolutional neural networks},
Author = {Zabawa, Laura and Kicherer, Anna and Klingbeil, Lasse and T{\"o}pfer, Reinhard and Kuhlmann, Heiner and Roscher, Ribana},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2020},
Pages = {73--83},
Volume = {164},
Publisher = {Elsevier}
}
  • J. Bömer, L. Zabawa, P. Sieren, A. Kicherer, L. Klingbeil, U. Rascher, O. Müller, H. Kuhlmann, and R. Roscher, “Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks,” in Proc. of the Computer Vision – ECCV Workshops, Cham, 2020, p. 347–359.
    [Bibtex] 
    @InProceedings{Boemer2020grapevine,
author = {B{\"o}mer, Jonas and Zabawa, Laura and Sieren, Philipp and Kicherer, Anna and Klingbeil, Lasse and Rascher, Uwe and M{\"u}ller, Onno and Kuhlmann, Heiner and Roscher, Ribana},
title = {Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks},
booktitle = {Proc. of the Computer Vision -- ECCV Workshops},
year = {2020},
editor = {Bartoli, Adrien and Fusiello, Andrea},
pages = {347--359},
address = {Cham},
publisher = {Springer International Publishing},
abstract = {Knowledge about the damage of grapevine berries in the vineyard is important for breeders and farmers. Damage to berries can be caused for example by mechanical machines during vineyard management, various diseases, parasites or abiotic stress like sun damage. The manual detection of damaged berries in the field is a subjective and labour-intensive task, and automatic detection by machine learning methods is challenging if all variants of damage should be modelled. Our proposed method detects regions of damaged berries in images in an efficient and objective manner using a shallow neural network, where the severeness of the damage is visualized with a heatmap.},
isbn = {978-3-030-65414-6},
}

2019

  • [PDF] A. Foerster, J. Behley, J. Behmann, and R. Roscher, “Hyperspectral plant disease forecasting using generative adversarial networks,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019.
    [Bibtex] 
    @InProceedings{foerster2019,
author = {Foerster, A. and Behley, J. and Behmann, J. and Roscher, R.},
title = {Hyperspectral plant disease forecasting using generative adversarial networks},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2019},
date-added = {2019-05-26 21:01:17 +0200},
date-modified = {2019-05-26 21:07:38 +0200},
}
  • [PDF] L. Strothmann, U. Rascher, and R. Roscher, “Detection of anomalous grapevine berries using all-convolutional autoencoders,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARS), 2019.
    [Bibtex] 
    @InProceedings{strothmann2019,
author = {Strothmann, Laurenz and Rascher, Uwe and Roscher, Ribana},
title = {Detection of anomalous grapevine berries using all-convolutional autoencoders},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARS)},
year = {2019},
date-added = {2019-05-26 21:09:56 +0200},
date-modified = {2019-05-26 21:25:51 +0200},
}
  • [PDF] L. Zabawa, A. Kicherer, L. Klingbeil, A. Milioto, R. Töpfer, H. Kuhlmann, and R. Roscher, “Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks,” in Proc. of the CVPR Workshop on Computer Vision Problems in Plant Phenotyping, 2019.
    [Bibtex] 
    @InProceedings{zabawa2019,
author = {Zabawa, Laura and Kicherer, Anna and Klingbeil, Lasse and Milioto, Andres and T{\"o}pfer, Reinhard and Kuhlmann, Heiner and Roscher, Ribana},
title = {Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks},
booktitle = {Proc. of the CVPR Workshop on Computer Vision Problems in Plant Phenotyping},
year = {2019},
date-added = {2019-05-26 21:11:29 +0200},
date-modified = {2019-05-26 21:23:12 +0200},
}
  • J. Kierdorf, L. Zabawa, L. Lucks, L. Klingbeil, H. Kuhlmann, and others, “Detection and Counting of Wheat Ears by Means of Ground-based Image Acquisition.,” Bornimer Agrartechnische Berichte, iss. 102, p. 158–167, 2019.
    [Bibtex] 
    @article{kierdorf2019detection,
title={Detection and Counting of Wheat Ears by Means of Ground-based Image Acquisition.},
author={Kierdorf, J and Zabawa, L and Lucks, L and Klingbeil, L and Kuhlmann, H and others},
journal={Bornimer Agrartechnische Berichte},
number={102},
pages={158--167},
year={2019},
publisher={Leibniz-Institut f{\"u}r Agrartechnik und Bio{\"o}konomie eV (ATB)}
}

2018

  • [PDF] L. Drees, R. Roscher, and S. Wenzel, “Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-Pixel Quantification,” Photogrammetric Engineering & Remote Sensing, vol. 84, iss. 5, pp. 279-286, 2018.
    [Bibtex] 
    @Article{Drees2018,
Title = {Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-Pixel Quantification},
Author = {Drees, Lukas and Roscher, Ribana and Wenzel, Susanne},
Journal = {Photogrammetric Engineering \& Remote Sensing},
Year = {2018},
Number = {5},
Pages = {279-286},
Volume = {84},
Bdsk-url-1 = {https://arxiv.org/abs/1802.02813},
Url = {https://arxiv.org/abs/1802.02813}
}
  • [PDF] K. Franz, R. Roscher, A. Milioto, S. Wenzel, and J. Kusche, “Ocean Eddy Identification and Tracking using Neural Networks,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018.
    [Bibtex] 
    @InProceedings{Franz2018,
author = {Franz, Katharina and Roscher, Ribana and Milioto, Andres and Wenzel, Susanne and Kusche, J{\"u}rgen},
title = {Ocean Eddy Identification and Tracking using Neural Networks},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium ({IGARSS})},
year = {2018},
url = {https://arxiv.org/pdf/1803.07436.pdf},
}
  • J. Oehrlein, A. Förster, D. Schunck, Y. Dehbi, R. Roscher, and J. -H. Haunert, “Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories,” in Proc~of the Conference on Smart Data and Smart Cities, 2018.
    [Bibtex] 
    @InProceedings{Oehrlein2018,
author = {Oehrlein, J. and F{\"o}rster, A. and Schunck, D. and Dehbi, Y. and Roscher, R. and Haunert, J.-H.},
title = {Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories},
booktitle = {Proc~of the Conference on Smart Data and Smart Cities},
year = {2018},
note = {Best Paper Award},
owner = {ribana},
timestamp = {2018.10.27},
}

2017

  • [PDF] [DOI] A. Bettge, R. Roscher, and S. Wenzel, “Deep self-taught learning for remote sensing image classification,” in Proc. of the Conference on Big Data from Space, 2017, pp. 301-304.
    [Bibtex] 
    @InProceedings{Bettge2017,
author = {Bettge, Anika and Roscher, Ribana and Wenzel, Susanne},
title = {Deep self-taught learning for remote sensing image classification},
booktitle = {Proc. of the Conference on Big Data from Space},
year = {2017},
pages = {301-304},
abstract = {This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our self-taught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple layers and use the output of the deepest layer as input to a classification algorithm. We evaluate our approach using a multispectral Landsat 5 TM image of a study area in the North of Novo Progresso (South America) and the Zurich Summer Data Set provided by the University of Zurich. Experiments indicate that features learned by a deep self-taught learning framework can be used for classification and improve the results compared to classification results using the original feature representation.},
bdsk-url-1 = {http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
bdsk-url-2 = {https://doi.org/10.2760/383579},
doi = {10.2760/383579},
url = {http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
}
  • [PDF] [DOI] A. Braakmann-Folgmann, R. Roscher, S. Wenzel, B. Uebbing, and J. Kusche, “Sea level anomaly prediction using recurrent neural networks,” in Proc. of the Conference on Big Data from Space, 2017, pp. 297-300.
    [Bibtex] 
    @InProceedings{Braakmann-Folgmann2017,
author = {Braakmann-Folgmann, Anne and Roscher, Ribana and Wenzel, Susanne and Uebbing, Bernd and Kusche, J\"urgen},
title = {Sea level anomaly prediction using recurrent neural networks},
booktitle = {Proc. of the Conference on Big Data from Space},
year = {2017},
pages = {297-300},
abstract = {Sea level change, one of the most dire impacts of anthropogenic global warming, will affect a large amount of the world's population. However, sea level change is not uniform in time and space, and the skill of conventional prediction methods is limited due to the ocean's internal variabi-lity on timescales from weeks to decades. Here we study the potential of neural network methods which have been used successfully in other applications, but rarely been applied for this task. We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to analyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA). We test our method for the northern and equatorial Pacific Ocean, using gridded altimeter-derived SLA data. We show that the used network designs outperform a simple regression and that adding a CNN improves the skill significantly. The predictions are stable over several years.},
bdsk-url-1 = {http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
bdsk-url-2 = {https://doi.org/10.2760/383579},
doi = {10.2760/383579},
url = {http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108361/jrc180361_procbids17.pdf},
}
  • [DOI] L. Drees and R. Roscher, “Archetypal Analysis for Sparse Representation-based Hyperspectral Sup-Pixel Quantification,” in Proc. of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017.
    [Bibtex] 
    @InProceedings{drees2017archetypal,
author = {Drees, Lukas and Roscher, Ribana},
title = {Archetypal Analysis for Sparse Representation-based Hyperspectral Sup-Pixel Quantification},
booktitle = {Proc. of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2017},
volume = {4},
bdsk-url-1 = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1-W1/133/2017/isprs-annals-IV-1-W1-133-2017.pdf},
bdsk-url-2 = {https://doi.org/10.5194/isprs-annals-IV-1-W1-133-2017},
doi = {10.5194/isprs-annals-IV-1-W1-133-2017},
url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1-W1/133/2017/isprs-annals-IV-1-W1-133-2017.pdf},
}
  • [PDF] [DOI] R. Hagensieker, R. Roscher, J. Rosentreter, B. Jakimow, and B. Waske, “Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data,” International Journal of Applied Earth Observation and Geoinformation, vol. 63, p. 244–256, 2017.
    [Bibtex] 
    @Article{Hagensieker2017,
Title = {Tropical land use land cover mapping in Par{\'a} (Brazil) using discriminative Markov random fields and multi-temporal Terra{SAR-X} data},
Author = {Hagensieker, Ron and Roscher, Ribana and Rosentreter, Johannes and Jakimow, Benjamin and Waske, Bj{\"o}rn},
Journal = {International Journal of Applied Earth Observation and Geoinformation},
Year = {2017},
Pages = {244--256},
Volume = {63},
Bdsk-url-1 = {https://arxiv.org/abs/1709.07794},
Bdsk-url-2 = {https://doi.org/10.1016/j.jag.2017.07.019},
Doi = {10.1016/j.jag.2017.07.019},
Publisher = {Elsevier},
Url = {https://arxiv.org/abs/1709.07794}
}
  • [PDF] R. Roscher, L. Drees, and S. Wenzel, “Sparse representation-based archetypal graphs for spectral clustering,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017.
    [Bibtex] 
    @InProceedings{Roscher2017,
author = {Roscher, Ribana and Drees, Lukas and Wenzel, Susanne},
title = {Sparse representation-based archetypal graphs for spectral clustering},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2017},
bdsk-url-1 = {https://www.researchgate.net/publication/321680475_Sparse_representation-based_archetypal_graphs_for_spectral_clustering},
url = {https://www.researchgate.net/publication/321680475_Sparse_representation-based_archetypal_graphs_for_spectral_clustering},
}
  • [PDF] [DOI] R. Roscher, B. Uebbing, and J. Kusche, “STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields,” Remote Sensing of Environment, vol. 201, p. 148–164, 2017.
    [Bibtex] 
    @Article{Roscher2017STAR,
Title = {{STAR}: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields},
Author = {Roscher, Ribana and Uebbing, Bernd and Kusche, J\"urgen},
Journal = {Remote Sensing of Environment},
Year = {2017},
Pages = {148--164},
Volume = {201},
Bdsk-url-1 = {https://doi.org/10.1016/j.rse.2017.07.024},
Doi = {10.1016/j.rse.2017.07.024}
}
  • [DOI] J. Rosentreter, R. Hagensieker, A. Okujeni, R. Roscher, and B. Waske, “Sub-pixel mapping of urban areas using EnMAP data and multioutput support vector regression,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017.
    [Bibtex] 
    @Article{Rosentreter2017Subpixel,
Title = {Sub-pixel mapping of urban areas using {EnMAP} data and multioutput support vector regression},
Author = {Rosentreter, Johannes and Hagensieker, Ron and Okujeni, Akpona and Roscher, Ribana and Waske, Bj\"orn},
Journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
Year = {2017},
Bdsk-url-1 = {https://doi.org/10.1109/JSTARS.2017.2652726},
Doi = {10.1109/JSTARS.2017.2652726}
}

2016

  • [DOI] B. Franke, J. Plante, R. Roscher, A. Lee, C. Smyth, A. Hatefi, F. Chen, E. Gil, A. Schwing, A. Selvitella, M. M. Hoffman, R. Grosse, D. Hendricks, and N. Reid, “Statistical Inference, Learning and Models in Big Data,” International Statistical Review, vol. 84, iss. 3, pp. 371-389, 2016.
    [Bibtex] 
    @Article{Franke2016BigData,
Title = {Statistical Inference, Learning and Models in Big Data},
Author = {Franke, Beate and Plante, Jean-Fran\c{c}ois and Roscher, Ribana and Lee, Annie and Smyth, Cathal and Hatefi, Armin and Chen, Fuqi and Gil, Einat and Schwing, Alex and Selvitella, Alessandro and Hoffman, Michael M. and Grosse, Roger and Hendricks, Dieter and Reid, Nancy},
Journal = {International Statistical Review},
Year = {2016},
Number = {3},
Pages = {371-389},
Volume = {84},
Abstract = {Big data provides big opportunities for statistical inference, but perhaps even bigger challenges, often related to differences in volume, variety, velocity, and veracity of information when compared to smaller carefully collected datasets. From January to June, 2015, the Canadian Institute of Statistical Sciences organized a thematic program on Statistical Inference, Learning and Models in Big Data. This paper arose from presentations and discussions that took place during the thematic program.},
Bdsk-url-1 = {https://doi.org/10.1111/insr.12176/full},
Doi = {10.1111/insr.12176/full}
}
  • [DOI] B. Mack, R. Roscher, S. Stenzel, H. Feilhauer, S. Schmidtlein, and B. Waske, “Mapping raised bogs with an iterative one-class classification approach,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 120, p. 53–64, 2016.
    [Bibtex] 
    @Article{Mack2016Raised,
Title = {Mapping raised bogs with an iterative one-class classification approach},
Author = {Mack, Benjamin and Roscher, Ribana and Stenzel, Stefanie and Feilhauer, Hannes and Schmidtlein, Sebastian and Waske, Bj{\"o}rn},
Journal = {{ISPRS} Journal of Photogrammetry and Remote Sensing},
Year = {2016},
Pages = {53--64},
Volume = {120},
Abstract = {Abstract Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative \{OCC\} approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative \{OCC\} outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier. },
Bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0924271616302180},
Bdsk-url-2 = {https://doi.org/10.1016/j.isprsjprs.2016.07.008},
Doi = {10.1016/j.isprsjprs.2016.07.008},
ISSN = {0924-2716},
Keywords = {Remote sensing},
Url = {http://www.sciencedirect.com/science/article/pii/S0924271616302180}
}
  • [DOI] R. Roscher, J. Behmann, A. Mahlein, J. Dupuis, H. Kuhlmann, and L. Plümer, “Detection of Disease Symptoms on Hyperspectral 3D Plant Models,” in Proc. of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, p. 89–96.
    [Bibtex] 
    @InProceedings{Roscher2016detection,
author = {Roscher, Ribana and Behmann, Jan and Mahlein, Anne-Kathrin and Dupuis, Jan and Kuhlmann, Heiner and Pl{\"u}mer, Lutz},
title = {Detection of Disease Symptoms on Hyperspectral {3D} Plant Models},
booktitle = {Proc. of the {ISPRS} Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2016},
pages = {89--96},
abstract = {We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.},
bdsk-url-1 = {https://www.researchgate.net/publication/303975671_On_the_Benefit_of_Topographic_Dictionaries_for_Detecting_Disease_Symptoms_on_Hyperspectral_3D_Plant_Models},
bdsk-url-2 = {https://doi.org/10.5194/isprs-annals-III-7-89-2016},
doi = {10.5194/isprs-annals-III-7-89-2016},
url = {https://www.researchgate.net/publication/303975671_On_the_Benefit_of_Topographic_Dictionaries_for_Detecting_Disease_Symptoms_on_Hyperspectral_3D_Plant_Models},
}
  • [DOI] R. Roscher, J. Behmann, A. Mahlein, and L. Plümer, “On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models,” in Proc. of the Workshop on Hyperspectral Image and Signal Processing, 2016.
    [Bibtex] 
    @InProceedings{Roscher2016Topographic,
author = {Roscher, Ribana and Behmann, Jan and Mahlein, Anne-Kathrin and Pl{\"u}mer, Lutz},
title = {On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral {3D} Plant Models},
booktitle = {Proc. of the Workshop on Hyperspectral Image and Signal Processing},
year = {2016},
abstract = {We analyze the benefit of using topographic dictionaries for a sparse representation (SR) approach for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Topographic dictionaries are an arranged set of basis elements in which neighbored dictionary elements tend to cause similar activations in the SR approach. In this paper, the dictionary is obtained from samples of a healthy plant and partly build in a topographic way by using hyperspectral as well as geometry information, i.e. depth and inclination. It turns out that hyperspectral signals of leafs show a typical structure depending on depth and inclination and thus, both influences can be disentangled in our approach. Rare signals which do not fit into this model, e.g. leaf veins, are also captured in the dictionary in a non-topographic way. A reconstruction error index is used as indicator, in which disease symptoms can be distinguished from healthy plant regions.nThe advantage of the presented approach is that full spectral and geometry information is needed only once to built the dictionary, whereas the sparse reconstruction is done solely on hyperspectral information.},
bdsk-url-1 = {http://ieeexplore.ieee.org/document/8071690/},
bdsk-url-2 = {https://doi.org/10.1109/WHISPERS.2016.8071690},
doi = {10.1109/WHISPERS.2016.8071690},
url = {http://ieeexplore.ieee.org/document/8071690/},
}
  • [DOI] R. Roscher, S. Wenzel, and B. Waske, “Discriminative archetypal self-taught learning for multispectral landcover classification,” in Proc. of the IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), 2016, p. 1–5.
    [Bibtex] 
    @InProceedings{roscher2016discriminative,
author = {Roscher, Ribana and Wenzel, Susanne and Waske, Bj\"orn},
title = {Discriminative archetypal self-taught learning for multispectral landcover classification},
booktitle = {Proc. of the {IAPR} Workshop on Pattern Recogniton in Remote Sensing ({PRRS})},
year = {2016},
pages = {1--5},
bdsk-url-1 = {http://ieeexplore.ieee.org/document/7867022/},
bdsk-url-2 = {https://doi.org/10.1109/PRRS.2016.7867022},
doi = {10.1109/PRRS.2016.7867022},
url = {http://ieeexplore.ieee.org/document/7867022/},
}
  • [DOI] T. Schubert, S. Wenzel, R. Roscher, and C. Stachniss, “Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging,” in Proc. of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, p. 97–102.
    [Bibtex] 
    @InProceedings{schubert2016investigation,
author = {Schubert, Till and Wenzel, Susanne and Roscher, Ribana and Stachniss, Cyrill},
title = {Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging},
booktitle = {Proc. of the {ISPRS} Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2016},
pages = {97--102},
bdsk-url-1 = {https://doi.org/10.5194/isprs-annals-III-7-97-2016},
doi = {10.5194/isprs-annals-III-7-97-2016},
}
  • B. Uebbing, R. Roscher, and J. Kusche, “Evaluating coastal sea surface heights based on a novel sub-waveform approach using sparse representation and conditional random fields,” in Proc. of the EGU General Assembly Conference Abstracts, 2016, p. 11729.
    [Bibtex] 
    @InProceedings{uebbing2016evaluating,
author = {Uebbing, Bernd and Roscher, Ribana and Kusche, J{\"u}rgen},
title = {Evaluating coastal sea surface heights based on a novel sub-waveform approach using sparse representation and conditional random fields},
booktitle = {Proc. of the {EGU} General Assembly Conference Abstracts},
year = {2016},
volume = {18},
pages = {11729},
}

2015

  • [DOI] R. Roscher, C. Römer, B. Waske, and L. Plümer, “Landcover classification with self-taught learning on archetypal dictionaries,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, p. 2358–2361.
    [Bibtex] 
    @InProceedings{Roscher2015Selftaught,
author = {Roscher, Ribana and R\"omer, Christoph and Waske, Bj\"orn and Pl\"umer, Lutz},
title = {Landcover classification with self-taught learning on archetypal dictionaries},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2015},
pages = {2358--2361},
bdsk-url-1 = {https://doi.org/10.1109/IGARSS.2015.7326282},
doi = {10.1109/IGARSS.2015.7326282},
}
  • [DOI] R. Roscher, B. Uebbing, and J. Kusche, “Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, p. 1234–1237.
    [Bibtex] 
    @InProceedings{Roscher2015Altimeter,
author = {Roscher, R. and Uebbing, B. and Kusche, J.},
title = {Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
year = {2015},
pages = {1234--1237},
bdsk-url-1 = {https://doi.org/10.1109/IGARSS.2015.7325996},
doi = {10.1109/IGARSS.2015.7325996},
}
  • R. Roscher, B. Uebbing, and J. Kusche, “Improving Sea Surface Height Estimation Using Spatio-Temporal Altimeter Waveform Retracking via Sparse Representation and Conditional Random Fields,” in ESA-Earth Observation for Water Cycle Science, 2015.
    [Bibtex] 
    @InProceedings{roscher2015improving,
author = {Roscher, Ribana and Uebbing, Bernd and Kusche, J{\"u}rgen},
title = {Improving Sea Surface Height Estimation Using Spatio-Temporal Altimeter Waveform Retracking via Sparse Representation and Conditional Random Fields},
booktitle = {{ESA}-Earth Observation for Water Cycle Science},
year = {2015},
}
  • [DOI] R. Roscher and B. Waske, “Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, iss. 3, p. 1623–1634, 2015.
    [Bibtex] 
    @Article{Roscher2015Shapelet,
Title = {Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images},
Author = {Roscher, Ribana and Waske, Bj\"orn},
Journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
Year = {2015},
Number = {3},
Pages = {1623--1634},
Volume = {54},
Abstract = {This paper presents a sparse-representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary, sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way, and spectral information is embodied by training samples. A combination of shapelets and spectral information is represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image data sets. They illustrate that our proposed approach shows superior results in comparison to sparse-representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse-representation-based classifiers.},
Bdsk-url-1 = {https://doi.org/10.1109/TGRS.2015.2484619},
Doi = {10.1109/TGRS.2015.2484619},
ISSN = {0196-2892}
}

2014

  • R. Hagensieker, R. Roscher, and B. Waske, “Texture-based classification of a tropical forest area using multi-temporal ASAR data,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014.
    [Bibtex] 
    @InProceedings{Hagensieker2014Texture,
author = {Hagensieker, Ron and Roscher, Ribana and Waske, Bj{\"o}rn},
title = {Texture-based classification of a tropical forest area using multi-temporal ASAR data},
booktitle = {Proc. of the IEEE International Geoscience and Remote Sensing Symposium ({IGARSS})},
year = {2014},
abstract = {[none]},
}
  • K. Herzog, R. Roscher, M. Wieland, A. Kicherer, T. Läbe, W. Förstner, H. Kuhlmann, and R. Töpfer, “Initial steps for high-throughput phenotyping in vineyards,” VITIS-Journal of Grapevine Research, vol. 53, iss. 1, p. 1, 2014.
    [Bibtex] 
    @Article{herzog2014initial,
Title = {Initial steps for high-throughput phenotyping in vineyards},
Author = {Herzog, Katja and Roscher, Ribana and Wieland, Michael and Kicherer, Anna and L{\"a}be, Thomas and F{\"o}rstner, Wolfgang and Kuhlmann, Heiner and T{\"o}pfer, Reinhard},
Journal = {{VITIS}-Journal of Grapevine Research},
Year = {2014},
Number = {1},
Pages = {1},
Volume = {53}
}
  • [DOI] A. Kicherer, R. Roscher, K. Herzog, W. Förstner, and R. Töpfer, “Image based Evaluation for the Detection of Cluster Parameters in Grapevine,” in Proc. of the Acta horticulturae, 2014.
    [Bibtex] 
    @InProceedings{Kicherer2014Evaluation,
author = {Kicherer, Anna and Roscher, Ribana and Herzog, Katja and F\"orstner, Wolfgang and T\"opfer, Reinhard},
title = {Image based Evaluation for the Detection of Cluster Parameters in Grapevine},
booktitle = {Proc. of the Acta horticulturae},
year = {2014},
bdsk-url-1 = {http://www.actahort.org/books/1082/1082_46.htm},
bdsk-url-2 = {https://doi.org/10.17660/ActaHortic.2015.1082.46},
doi = {10.17660/ActaHortic.2015.1082.46},
url = {http://www.actahort.org/books/1082/1082_46.htm},
}
  • B. Mack, R. Roscher, and B. Waske, “Can I trust my one-class classification?,” Remote Sensing, vol. 6, iss. 9, p. 8779–8802, 2014.
    [Bibtex] 
    @Article{Mack2014Can,
Title = {Can I trust my one-class classification?},
Author = {Mack, Benjamin and Roscher, Ribana and Waske, Bj{\"o}rn},
Journal = {Remote Sensing},
Year = {2014},
Number = {9},
Pages = {8779--8802},
Volume = {6},
Abstract = {Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion.},
Bdsk-url-1 = {http://www.ipb.uni-bonn.de/pdfs/Mack2014Can.pdf},
Url = {http://www.ipb.uni-bonn.de/pdfs/Mack2014Can.pdf}
}
  • [PDF] [DOI] R. Roscher, K. Herzog, A. Kunkel, A. Kicherer, R. Töpfer, and W. Förstner, “Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields,” Computers and Electronics in Agriculture, vol. 100, p. 148–158, 2014.
    [Bibtex] 
    @Article{Roscher2014Automated,
Title = {Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields},
Author = {Roscher, Ribana and Herzog, Katja and Kunkel, Annemarie and Kicherer, Anna and T{\"o}pfer, Reinhard and F{\"o}rstner, Wolfgang},
Journal = {Computers and Electronics in Agriculture},
Year = {2014},
Pages = {148--158},
Volume = {100},
Bdsk-url-1 = {https://doi.org/10.1016/j.compag.2013.11.008},
Doi = {10.1016/j.compag.2013.11.008},
Publisher = {Elsevier}
}
  • R. Roscher and B. Waske, “Shapelet-based sparse image representation for landcover classification of hyperspectral data,” in Proc. of the IAPR Workshop on Pattern Recognition in Remote Sensing, 2014, p. 1–6.
    [Bibtex] 
    @InProceedings{Roscher2014Shapelet,
author = {Roscher, Ribana and Waske, Bj{\"o}rn},
title = {Shapelet-based sparse image representation for landcover classification of hyperspectral data},
booktitle = {Proc. of the {IAPR} Workshop on Pattern Recognition in Remote Sensing},
year = {2014},
pages = {1--6},
abstract = {This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-specific spatial-spectral dictionary, which is used for a sparse coding procedure for the reconstruction and classification of image patches. Hereby, each image patch is sparsely represented by a linear combination of elements out of the dictionary. The set of shapelets is specifically learned for each image in an unsupervised way in order to capture the image structure. The spectral features are assumed to be the training data. The experiments show that the proposed approach shows superior results in comparison to sparse-representation based classifiers that use no or only limited spatial information and behaves competitive or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers.},
bdsk-url-1 = {http://www.ipb.uni-bonn.de/pdfs/Roscher2014Shapelet.pdf},
url = {http://www.ipb.uni-bonn.de/pdfs/Roscher2014Shapelet.pdf},
}
  • R. Roscher and B. Waske, “Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014, p. 3674–3677.
    [Bibtex] 
    @InProceedings{roscher2014superpixel,
author = {Roscher, Ribana and Waske, Bj{\"o}rn},
title = {Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields},
booktitle = {Proc. of the {IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})},
year = {2014},
pages = {3674--3677},
}

2013

  • A. Kicherer, K. Herzog, R. Roscher, M. Wieland, P. Rüger, H. Kuhlmann, H. Schwarz, and R. Töpfer, “High-Throughput Phenotyping of yield parameters in the vineyard–first steps,” in Proc. of the JKI Young Scientists Meeting, 2013.
    [Bibtex] 
    @InProceedings{kicherer2013high,
author = {Kicherer, Anna and Herzog, Katja and Roscher, Ribana and Wieland, Markus and R{\"u}ger, Philipp and Kuhlmann, Heiner and Schwarz, Hans-Peter and T{\"o}pfer, Reinhard},
title = {High-Throughput Phenotyping of yield parameters in the vineyard--first steps},
booktitle = {Proc. of the JKI Young Scientists Meeting},
year = {2013},
}
  • A. Kicherer, R. Roscher, K. Herzog, S. Šimon, W. Förstner, and R. Töpfer, “BAT (Berry Analysis Tool): A high-throughput image interpretation tool to acquire the number, diameter, and volume of grapevine berries,” VITIS-Journal of Grapevine Research, vol. 52, iss. 3, p. 129, 2013.
    [Bibtex] 
    @Article{kicherer2013bat,
Title = {BAT (Berry Analysis Tool): A high-throughput image interpretation tool to acquire the number, diameter, and volume of grapevine berries},
Author = {Kicherer, Anna and Roscher, Ribana and Herzog, Katja and {\v{S}}imon, Silvio and F{\"o}rstner, Wolfgang and T{\"o}pfer, Reinhard},
Journal = {{VITIS}-Journal of Grapevine Research},
Year = {2013},
Number = {3},
Pages = {129},
Volume = {52}
}

2012

  • A. Kicherer, R. Roscher, K. Herzog, W. Förster, and R. Töpfer, “HT-Phenotyping methods for yield parameters in grapevine,” Berichte aus dem Julius Kühn-Institut, iss. 167, p. 39, 2012.
    [Bibtex] 
    @Article{kicherer2012ht,
Title = {{HT}-Phenotyping methods for yield parameters in grapevine},
Author = {Kicherer, Anna and Roscher, Ribana and Herzog, Katja and F{\"o}rster, Wolfgang and T{\"o}pfer, Reinhard},
Journal = {Berichte aus dem Julius K{\"u}hn-Institut},
Year = {2012},
Number = {167},
Pages = {39}
}
  • R. Roscher, “Sequential Learning using Incremental Import Vector Machines for Semantic Segmentation,” PhD Thesis, 2012.
    [Bibtex] 
    @PhdThesis{Roscher2012Sequential,
Title = {Sequential Learning using Incremental Import Vector Machines for Semantic Segmentation},
Author = {Roscher, Ribana},
School = {Department of Photogrammetry, University of Bonn},
Year = {2012},
Abstract = {We propose an innovative machine learning algorithm called incremental import vector machines that is used for classification purposes. The classifier is specifically designed for the task of sequential learning, in which the data samples are successively presented to the classifier. The motivation for our work comes from the effort to formulate a classifier that can manage the major challenges of sequential learning problems, while being a powerful classifier in terms of classification accuracy, efficiency and meaningful output. One challenge of sequential learning is that data samples are not completely available to the learner at a given point of time and generally, waiting for a representative number of data is undesirable and impractical. Thus, in order to allow for a classification of given data samples at any time, the learning phase of the classifier model needs to start immediately, even if not all training samples are available. Another challenge is that the number of sequential arriving data samples can be very large or even infinite and thus, not all samples can be stored. Furthermore, the distribution of the sample can vary over time and the classifier model needs to remain stable and unchanged to irrelevant samples while being plastic to new, important samples. Therefore our key contribution is to develop, analyze and evaluate a powerful incremental learner for sequential learning which we call incremental import vector machines (I2VMs). The classifier is based on the batch machine learning algorithm import vector machines, which was developed by Zhu and Hastie (2005). I2VM is a kernel-based, discriminative classifier and thus, is able to deal with complex data distributions. Additionally ,the learner is sparse for an efficient training and testing and has a probabilistic output. A key achievement of this thesis is the verification and analysis of the discriminative and reconstructive model components of IVM and I2VM. While discriminative classifiers try to separate the classes as well as possible, classifiers with a reconstructive component aspire to have a high information content in order to approximate the distribution of the data samples. Both properties are necessary for a powerful incremental classifier. A further key achievement is the formulation of the incremental learning strategy of I2VM. The strategy deals with adding and removing data samples and the update of the current set of model parameters. Furthermore, also new classes and features can be incorporated. The learning strategy adapts the model continuously, while keeping it stable and efficient. In our experiments we use I2VM for the semantic segmentation of images from an image database, for large area land cover classification of overlapping remote sensing images and for object tracking in image sequences. We show that I2VM results in superior or competitive classification accuracies to comparable classifiers. A substantial achievement of the thesis is that I2VM's performance is independent of the ordering of the data samples and a reconsidering of already encountered samples for learning is not necessary. A further achievement is that I2VM is able to deal with very long data streams without a loss in the efficiency. Furthermore, as another achievement, we show that I2VM provide reliable posterior probabilities since samples with high class probabilities are accurately classified, whereas relatively low class probabilities are more likely referred to misclassified samples.},
Bdsk-url-1 = {http://hss.ulb.uni-bonn.de/2012/3009/3009.htm},
City = {Bonn},
Url = {http://hss.ulb.uni-bonn.de/2012/3009/3009.htm}
}
  • [DOI] R. Roscher, W. Förstner, and B. Waske, “I$^2$VM: Incremental import vector machines,” Image and Vision Computing, vol. 30, iss. 4-5, p. 263–278, 2012.
    [Bibtex] 
    @Article{Roscher2012I2VM,
Title = {I$^2$VM: Incremental import vector machines},
Author = {Roscher, Ribana and F\"orstner, Wolfgang and Waske, Bj\"orn},
Journal = {Image and Vision Computing},
Year = {2012},
Number = {4-5},
Pages = {263--278},
Volume = {30},
Abstract = {We introduce an innovative incremental learner called incremental import vector machines ((IVM)-V-2). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental teaming. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how (IVM)-V-2 is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions. (C) 2012 Elsevier B.V. All rights reserved.},
Bdsk-url-1 = {https://doi.org/10.1016/j.imavis.2012.04.004},
Doi = {10.1016/j.imavis.2012.04.004},
Sn = {0262-8856},
Tc = {0},
Ut = {WOS:000305726700001},
Z8 = {0},
Z9 = {0},
Zb = {0}
}
  • [DOI] R. Roscher, B. Waske, and W. Förstner, “Incremental Import Vector Machines for Classifying Hyperspectral Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, iss. 9, p. 3463–3473, 2012.
    [Bibtex] 
    @Article{Roscher2012Incremental,
Title = {Incremental Import Vector Machines for Classifying Hyperspectral Data},
Author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
Journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
Year = {2012},
Number = {9},
Pages = {3463--3473},
Volume = {50},
Abstract = {In this paper, we propose an incremental learning strategy for import vector machines (IVM), which is a sparse kernel logistic regression approach. We use the procedure for the concept of self-training for sequential classification of hyperspectral data. The strategy comprises the inclusion of new training samples to increase the classification accuracy and the deletion of noninformative samples to be memory and runtime efficient. Moreover, we update the parameters in the incremental IVM model without retraining from scratch. Therefore, the incremental classifier is able to deal with large data sets. The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy, and experiments are conducted to assess the potential of the probabilistic outputs of the IVM. Experimental results demonstrate that the IVM and SVM perform similar in terms of classification accuracy. However, the number of import vectors is significantly lower when compared to the number of support vectors, and thus, the computation time during classification can be decreased. Moreover, the probabilities provided by IVM are more reliable, when compared to the probabilistic information, derived from an SVM's output. In addition, the proposed self-training strategy can increase the classification accuracy. Overall, the IVM and its incremental version is worthwhile for the classification of hyperspectral data.},
Bdsk-url-1 = {https://doi.org/10.1109/TGRS.2012.2184292},
Doi = {10.1109/TGRS.2012.2184292},
ISSN = {0196-2892}
}

2011

  • [DOI] R. Roscher, B. Waske, and W. Förstner, “Incremental import vector machines for large area land cover classification,” in Proc. of the IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011.
    [Bibtex] 
    @InProceedings{Roscher2011Incremental,
author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
title = {Incremental import vector machines for large area land cover classification},
booktitle = {Proc. of the {IEEE} International Conference on Computer Vision Workshops ({ICCV} Workshops)},
year = {2011},
abstract = {The classification of large areas consisting of multiple scenes is challenging regarding the handling of large and therefore mostly inhomogeneous data sets. Moreover, large data sets demand for computational efficient methods. We propose a method, which enables the efficient multi-class classification of large neighboring Landsat scenes. We use an incremental realization of the import vector machines, called I2VM, in combination with self-training to update an initial learned classifier with new training data acquired in the overlapping areas between neighboring Landsat scenes. We show in our experiments, that I2VM is a suitable classifier for large area land cover classification.},
bdsk-url-1 = {https://doi.org/10.1109/ICCVW.2011.6130249},
doi = {10.1109/ICCVW.2011.6130249},
keywords = {incremental import vector machines;inhomogeneous data sets;land cover classification;neighboring Landsat scenes;scenes classification;training data acquisition;data acquisition;geophysical image processing;image classification;natural scenes;support vector machines;terrain mapping;},
}
  • [DOI] B. Waske, R. Roscher, and S. Klemenjak, “Import Vector Machines Based Classification of Multisensor Remote Sensing Data,” in Proc. of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011.
    [Bibtex] 
    @InProceedings{Waske2011Import,
author = {Waske, Bj\"orn and Roscher, Ribana and Klemenjak, Sascha},
title = {Import Vector Machines Based Classification of Multisensor Remote Sensing Data},
booktitle = {Proc. of the {IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})},
year = {2011},
abstract = {The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (h) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is compared to Support Vector Machines (SVM) in terms of accuracy and complexity. In general, the experimental results demonstrate that the classification accuracy is improved by the multisensor data set. Moreover, IVM and SVM perform similar in terms of the classification accuracy. However, the number of import vectors is considerably less than the number of support vectors, and thus the computation time of the IVM classification is lower. IVM can directly be applied to the multi-class problems and provide probabilistic outputs. Overall IVM constitutes a feasible method and alternative to SVM.},
bdsk-url-1 = {https://doi.org/10.1109/IGARSS.2011.6049829},
doi = {10.1109/IGARSS.2011.6049829},
keywords = {Envisat ASAR ERS-2 SAR data;IVM;Landsat 5 TM scene;RapidEye scene;SVM comparison;TerraSAR-X data;computation time;data classification;import vector machines;multisensor remote sensing data;multispectral data;multitemporal SAR data;support vector machines;geophysical image processing;image classification;knowledge engineering;radar imaging;remote sensing by radar;spaceborne radar;synthetic aperture radar;},
}

2010

  • [DOI] R. Roscher, F. Schindler, and W. Förstner, “High Dimensional Correspondences from Low Dimensional Manifolds – An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms,” in Proc. of the 3\textsuperscriptrd International Workshop on Subspace Methods, in conjunction with ACCV2010, 2010, p. 10.
    [Bibtex] 
    @InProceedings{Roscher2010High,
author = {Roscher, Ribana and Schindler, Falko and F\"orstner, Wolfgang},
title = {High Dimensional Correspondences from Low Dimensional Manifolds -- An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms},
booktitle = {Proc. of the 3\textsuperscript{rd} International Workshop on Subspace Methods, in conjunction with ACCV2010},
year = {2010},
pages = {10},
abstract = {We discuss the utility of dimensionality reduction algorithms to put data points in high dimensional spaces into correspondence by learning a transformation between assigned data points on a lower dimensional structure. We assume that similar high dimensional feature spaces are characterized by a similar underlying low dimensional structure. To enable the determination of an affine transformation between two data sets we make use of well-known dimensional reduction algorithms. We demonstrate this procedure for applications like classification and assignments between two given data sets and evaluate six well-known algorithms during several experiments with different objectives. We show that with these algorithms and our transformation approach high dimensional data sets can be related to each other. We also show that linear methods turn out to be more suitable for assignment tasks, whereas graph-based methods appear to be superior for classification tasks.},
bdsk-url-1 = {http://www.ipb.uni-bonn.de/pdfs/Roscher2010High.pdf;Poster:Roscher2010High_Poster.pdf},
bdsk-url-2 = {https://doi.org/10.1007/978-3-642-22819-3_34},
doi = {10.1007/978-3-642-22819-3_34},
url = {http://www.ipb.uni-bonn.de/pdfs/Roscher2010High.pdf;Poster:Roscher2010High_Poster.pdf},
}
  • [DOI] R. Roscher, B. Waske, and W. Förstner, “Kernel discriminative random fields for land cover classification,” in Proc. of the IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 2010, p. 1–5.
    [Bibtex] 
    @InProceedings{roscher2010kernel,
author = {Roscher, Ribana and Waske, Bj{\"o}rn and F{\"o}rstner, Wolfgang},
title = {Kernel discriminative random fields for land cover classification},
booktitle = {Proc. of the IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)},
year = {2010},
pages = {1--5},
doi = {10.1109/PRRS.2010.5742801},
}

2009

  • M. Drauschke, R. Roscher, T. Läbe, and W. Förstner, “Improving image segmentation using multiple view analysis,” Object Extraction for 3D City Models, Road Databases and Traffic Monitoring-Concepts, Algorithms and Evaluatin (CMRT09), p. 211–216, 2009.
    [Bibtex] 
    @Article{drauschke2009improving,
Title = {Improving image segmentation using multiple view analysis},
Author = {Drauschke, Martin and Roscher, Ribana and L\"abe, T and F\"orstner, W},
Journal = {Object Extraction for 3D City Models, Road Databases and Traffic Monitoring-Concepts, Algorithms and Evaluatin (CMRT09)},
Year = {2009},
Pages = {211--216}
}