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Self-taught learning for land cover mapping of large areas, using multispectral remote sensing data

This research project aims on the development of a Self-taught Learning framework for the land cover classification of remote sensing data. The approach enables the use of labeled pixels (i.e., with reference information) and unlabeled pixels from arbitrary scenes and different acquisitions dates. In contrast to semi-supervised frameworks, the unlabeled data can contain unknown and irrelevant classes. Moreover, the classes need not to be explicitly modeled. The developed framework will be used for classifying remote sensing data from different study sites and sensors.

Project Duration: 08/2013 - 07/2016

Principal Investigator: Prof. Dr. Björn Waske

Projects staff: Dr. Ribana Roscher

Project partners:

-       University of Bonn, Institute of Geodesy and Geoinformation

Funding:  DFG - German Research Foundation (WA 2728/3-1)