Deep Self-taught learning
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.
 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.
 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.
 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
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
 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 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
 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.
 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.
 R. Roscher, L. Drees, and S. Wenzel, “Sparse representation-based archetypal graphs for spectral clustering,” in IEEE International Geoscience and Remote Sensing Symposium, 2017.