Research

Earth observation systems play an important role in the geoscientific community, because they regularly provide remote sensing data with a spectrally, spatially and temporally high resolution. These characteristics enable various applications to accurately monitor the earth’s landcover and its changes. Beside the challenge to deal with large amounts of data and limited class label information, current and future challenges comprise the definition and the way of integration of prior knowledge and the automatic determination of sophisticated feature representations.

Research interests:
– Landcover classification methods
– (Deep) Representation/Feature learning
– Self-taught learning
– Sparse representation for big data
– Incremental/Sequential learning

People

Prof. Dr.-Ing. Ribana Roscher

Junior Professor of Remote Sensing

Contact:
Email: ribana.roscher@uni-bonn.de
Tel: +49 – 228 – 73 – 27 16
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 2. OG, room 2.001

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Immanuel Weber

Doctoral student (external, Hochschule Koblenz)

Contact:
Email: immanuel.weber@hs-koblenz.de

Research area:
Autonomous object detection and scene interpretation in aerial images

Nisha Bhaskar

Doctoral student (external, APTIV Wuppertal)

Contact:
Email: nisha.bhaskar@aptiv.com

Research area:
Deep learning for pedestrian intent prediction in autonomous driving

 

Lukas Drees

Student assistant

Contact:
Email: s7ludree@uni-bonn.de

Current Research

Neural Networks for Analyzing Earth Observation Data

Global climate change plays an essential role in our daily life and is one of the most important topics, nowadays. Thus, the understanding, monitoring and prediction is essential to overcome related challenges. Neural networks are a powerful mean to solve tasks such as classification, detection and regression, where they show promising results reaching accuracies superior to classical and shallow state-of-art machine learning algorithms. However, so far they have been barely explored in the context of Earth observation data. In our research, we use the current advances in the deep learning area and adapt the methods for several applications in remote sensing.

Related publications:
[1] A. Braakmann-Folgmann, R. Roscher, S. Wenzel, B. Uebbing, and J. Kusche, “Sea level anomaly prediction using recurrent neural networks,” in Proc. Conference on Big Data from Space, 2017.
[2] K. Franz, R. Roscher, A. Milioto, S. Wenzel, and J. Kusche. “Ocean Eddy Identification and Tracking using Neural Networks.” accepted to IEEE International Geoscience and Remote Sensing Symposium (IGARSS), arXiv preprint arXiv:1803.07436, 2018.

Deep Self-taught learning

Self-taught learning with archetypal analysis

Self-taught learning (STL) has become a promising paradigm to exploit large amounts of unlabeled data for feature learning and classification. It utilizes both labeled and unlabeled data without the requirement that both sets have to share the same distribution and the same land use and land cover classes. In our research, we analyze the applicability of STL for the interpretation of remote sensing images. Moreover, we combine STL and the concepts of deep learning to learn valuable feature representations for remote sensing image classification.

Related publications:
[1] A. Bettge, R. Roscher, and S. Wenzel, “Deep self-taught learning for remote sensing image classification,” in Proc. Conference on Big Data from Space, 2017.
[2] R. Roscher, S. Wenzel, and B. Waske, “Discriminative archetypal self-taught learning for multispectral landcover classification,” in IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), 2016. 
[3] R. Roscher, C. Römer, B. Waske, and L. Plümer, “Landcover classification with self-taught learning on archetypal dictionaries,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 2358-2361.

Spatio-Temporal Altimeter Waveform Retracking

Spatio-Temporal Altimeter Waveform Retracking

Satellite radar altimetry is one of the most powerful techniques for measuring sea surface height variations, with applications ranging from operational oceanography to climate research. In this research we develop a novel spatio-temporal altimetry retracking technique which elements are the integration of information from spatially and temporally neighboring waveforms, a sub-waveform detection, where relevant sub-waveforms are separated from corrupted or non-relevant parts, and the identification the final best set of sea surfaces heights from multiple likely heights using a shortest-path algorithm.

Research partners: Satellite Geodesy Group, University of Bonn

Related publications:
[1] 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, pp. 148-164, 2017. 

Sparse Representation for Anomaly Detection

Sparse representation for anomaly detection

Sparse representation is a versatile tool, which can be used for many applications in the area of anomaly detection. We analyze, for example, the benefit of various structured dictionaries for sparse representation (SR), such as topographic dictionaries, for the detection of disease symptoms. The approach is used for the detection of Cercospora leaf spot disease symptoms on sugar beet plants in hyperspectral images. For the same application, sparse representation can be used to build sparse graphs for spectral clustering with highly distinctive clusters. The approach is used to identify clusters in close range hyperspectral images, where one or more clusters can be assigned to the disease symptoms. The approach shows also good performance for change detection in multispectral remote sensing images.

Research partners: INRES-Pflanzenkrankheiten und Pflanzenschutz, University of Bonn

Related publications:
[1] R. Roscher, J. Behmann, A. -K. Mahlein, J. Dupuis, H. Kuhlmann, and L. Plümer, “Detection of Disease Symptoms on Hyperspectral 3D Plant Models,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, pp. 89-96.
[2] R. Roscher, J. Behmann, A. -K. Mahlein, and L. Plümer, “On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models,” in Workshop on Hyperspectral Image and Signal Processing, 2016.
[3] R. Roscher, L. Drees, and S. Wenzel, “Sparse representation-based archetypal graphs for spectral clustering,” in IEEE International Geoscience and Remote Sensing Symposium, 2017.

Teaching

Current Teaching Activities
  • Advanced Machine Learning (Lecture & Seminar, MSc, summer)
  • Remote Sensing Image Analysis and Interpretation (Lecture & Practical Course, BSc, winter, eCampus)
Previous Teaching Activities
  • Big Data Analysis (Lecture & Seminar, MSc, summer)
  • Remote Sensing Image Interpretation (Practical Course, MSc, winter, eCampus)
Bachelor and Master Theses

Publications

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},
    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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2018.
    [Bibtex]
    @InProceedings{Franz2018,
    Title = {Ocean Eddy Identification and Tracking using Neural Networks},
    Author = {Franz, Katharina and Roscher, Ribana and Milioto, Andres and Wenzel, Susanne and Kusche, J{\"u}rgen},
    Booktitle = {{IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})},
    Year = {2018},
    Url = {https://arxiv.org/pdf/1803.07436.pdf}
    }

2017

  • [PDF] [DOI] A. Bettge, R. Roscher, and S. Wenzel, “Deep self-taught learning for remote sensing image classification,” in Proc. Conference on Big Data from Space, 2017, pp. 301-304.
    [Bibtex]
    @InProceedings{Bettge2017,
    Title = {Deep self-taught learning for remote sensing image classification},
    Author = {Bettge, Anika and Roscher, Ribana and Wenzel, Susanne},
    Booktitle = {Proc. 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.},
    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. Conference on Big Data from Space, 2017, pp. 297-300.
    [Bibtex]
    @InProceedings{Braakmann-Folgmann2017,
    Title = {Sea level anomaly prediction using recurrent neural networks},
    Author = {Braakmann-Folgmann, Anne and Roscher, Ribana and Wenzel, Susanne and Uebbing, Bernd and Kusche, J\"urgen},
    Booktitle = {Proc. 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.},
    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 ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017.
    [Bibtex]
    @InProceedings{drees2017archetypal,
    Title = {Archetypal Analysis for Sparse Representation-based Hyperspectral Sup-Pixel Quantification},
    Author = {Drees, Lukas and Roscher, Ribana},
    Booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
    Year = {2017},
    Volume = {4},
    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, pp. 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},
    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 IEEE International Geoscience and Remote Sensing Symposium, 2017.
    [Bibtex]
    @InProceedings{Roscher2017,
    Title = {Sparse representation-based archetypal graphs for spectral clustering},
    Author = {Roscher, Ribana and Drees, Lukas and Wenzel, Susanne},
    Booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
    Year = {2017},
    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, pp. 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},
    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},
    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.},
    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, pp. 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. },
    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 ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, pp. 89-96.
    [Bibtex]
    @InProceedings{Roscher2016detection,
    Title = {Detection of Disease Symptoms on Hyperspectral {3D} Plant Models},
    Author = {Roscher, Ribana and Behmann, Jan and Mahlein, Anne-Kathrin and Dupuis, Jan and Kuhlmann, Heiner and Pl{\"u}mer, Lutz},
    Booktitle = {{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.},
    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 Workshop on Hyperspectral Image and Signal Processing, 2016.
    [Bibtex]
    @InProceedings{Roscher2016Topographic,
    Title = {On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral {3D} Plant Models},
    Author = {Roscher, Ribana and Behmann, Jan and Mahlein, Anne-Kathrin and Pl{\"u}mer, Lutz},
    Booktitle = {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.},
    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 IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), 2016, pp. 1-5.
    [Bibtex]
    @InProceedings{roscher2016discriminative,
    Title = {Discriminative archetypal self-taught learning for multispectral landcover classification},
    Author = {Roscher, Ribana and Wenzel, Susanne and Waske, Bj\"orn},
    Booktitle = {{IAPR} Workshop on Pattern Recogniton in Remote Sensing ({PRRS})},
    Year = {2016},
    Pages = {1--5},
    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 ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, pp. 97-102.
    [Bibtex]
    @InProceedings{schubert2016investigation,
    Title = {Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging},
    Author = {Schubert, Till and Wenzel, Susanne and Roscher, Ribana and Stachniss, Cyrill},
    Booktitle = {{ISPRS} Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
    Year = {2016},
    Pages = {97--102},
    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 EGU General Assembly Conference Abstracts, 2016, p. 11729.
    [Bibtex]
    @InProceedings{uebbing2016evaluating,
    Title = {Evaluating coastal sea surface heights based on a novel sub-waveform approach using sparse representation and conditional random fields},
    Author = {Uebbing, Bernd and Roscher, Ribana and Kusche, J{\"u}rgen},
    Booktitle = {{EGU} General Assembly Conference Abstracts},
    Year = {2016},
    Pages = {11729},
    Volume = {18}
    }

2015

  • [DOI] R. Roscher, C. Römer, B. Waske, and L. Plümer, “Landcover classification with self-taught learning on archetypal dictionaries,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 2358-2361.
    [Bibtex]
    @InProceedings{Roscher2015Selftaught,
    Title = {Landcover classification with self-taught learning on archetypal dictionaries},
    Author = {Roscher, Ribana and R\"omer, Christoph and Waske, Bj\"orn and Pl\"umer, Lutz},
    Booktitle = {{IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})},
    Year = {2015},
    Pages = {2358--2361},
    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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 1234-1237.
    [Bibtex]
    @InProceedings{Roscher2015Altimeter,
    Title = {Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields},
    Author = {Roscher, R. and Uebbing, B. and Kusche, J.},
    Booktitle = {{IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})},
    Year = {2015},
    Pages = {1234--1237},
    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,
    Title = {Improving Sea Surface Height Estimation Using Spatio-Temporal Altimeter Waveform Retracking via Sparse Representation and Conditional Random Fields},
    Author = {Roscher, Ribana and Uebbing, Bernd and Kusche, J{\"u}rgen},
    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, pp. 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.},
    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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014.
    [Bibtex]
    @InProceedings{Hagensieker2014Texture,
    Title = {Texture-based classification of a tropical forest area using multi-temporal ASAR data},
    Author = {Hagensieker, Ron and Roscher, Ribana and Waske, Bj{\"o}rn},
    Booktitle = {{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 Acta horticulturae, 2014.
    [Bibtex]
    @InProceedings{Kicherer2014Evaluation,
    Title = {Image based Evaluation for the Detection of Cluster Parameters in Grapevine},
    Author = {Kicherer, Anna and Roscher, Ribana and Herzog, Katja and F\"orstner, Wolfgang and T\"opfer, Reinhard},
    Booktitle = {Acta horticulturae},
    Year = {2014},
    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, pp. 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.},
    Url = {http://www.ipb.uni-bonn.de/pdfs/Mack2014Can.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, pp. 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},
    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 IAPR Workshop on Pattern Recognition in Remote Sensing, 2014, pp. 1-6.
    [Bibtex]
    @InProceedings{Roscher2014Shapelet,
    Title = {Shapelet-based sparse image representation for landcover classification of hyperspectral data},
    Author = {Roscher, Ribana and Waske, Bj{\"o}rn},
    Booktitle = {{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.},
    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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014, pp. 3674-3677.
    [Bibtex]
    @InProceedings{roscher2014superpixel,
    Title = {Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields},
    Author = {Roscher, Ribana and Waske, Bj{\"o}rn},
    Booktitle = {{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 Young Scientists Meeting, 2013.
    [Bibtex]
    @InProceedings{kicherer2013high,
    Title = {High-Throughput Phenotyping of yield parameters in the vineyard--first steps},
    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},
    Booktitle = {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.},
    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, pp. 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.},
    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, pp. 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.},
    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 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011.
    [Bibtex]
    @InProceedings{Roscher2011Incremental,
    Title = {Incremental import vector machines for large area land cover classification},
    Author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
    Booktitle = {{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.},
    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 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011.
    [Bibtex]
    @InProceedings{Waske2011Import,
    Title = {Import Vector Machines Based Classification of Multisensor Remote Sensing Data},
    Author = {Waske, Bj\"orn and Roscher, Ribana and Klemenjak, Sascha},
    Booktitle = {{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.},
    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 The 3\textsuperscriptrd International Workshop on Subspace Methods, in conjunction with ACCV2010, 2010, p. 10.
    [Bibtex]
    @InProceedings{Roscher2010High,
    Title = {High Dimensional Correspondences from Low Dimensional Manifolds -- An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms},
    Author = {Roscher, Ribana and Schindler, Falko and F\"orstner, Wolfgang},
    Booktitle = {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.},
    Doi = {10.1007/978-3-642-22819-3_34},
    Url = {http://www.ipb.uni-bonn.de/pdfs/Roscher2010High.pdf;Poster:Roscher2010High_Poster.pdf}
    }
  • R. Roscher, B. Waske, and W. Förstner, “Kernel discriminative random fields for land cover classification,” in Pattern Recognition in Remote Sensing (PRRS), 2010 IAPR Workshop on, 2010, pp. 1-5.
    [Bibtex]
    @InProceedings{roscher2010kernel,
    Title = {Kernel discriminative random fields for land cover classification},
    Author = {Roscher, Ribana and Waske, Bj{\"o}rn and F{\"o}rstner, Wolfgang},
    Booktitle = {Pattern Recognition in Remote Sensing (PRRS), 2010 IAPR Workshop on},
    Year = {2010},
    Pages = {1--5}
    }

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), pp. 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}
    }