Current Research

Self-taught learning with archetypal analysis

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 this context, archetypal analysis is used to extract the most valuable unlabeled data which is used in the STL framework to learn an efficient and discriminative feature representation. In our research the approach is used for land cover classification of multispectral images.

Research partners: Remote Sensing and Geoinformatics Working Group, FU Berlin

Sparse representation for anomaly detection

Sparse representation for anomaly detection

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 this context, archetypal analysis is used to extract the most valuable unlabeled data which is used in the STL framework to learn an efficient and discriminative feature representation. In our research the approach is used for land cover classification of multispectral images.

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

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