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

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Earth Observation data play a major role in supporting decision-support systems and monitoring compliance of several multilateral environmental treaties. Land cover maps of remote sensing data are the most commonly used product in this context and the development of feasible and accurate classification strategies is an ongoing research field. Particularly the classification of larger areas is often challenging, e.g., due to the lack of adequate amount of training and validation 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 multispectral remote sensing data from different study sites, e.g., which are characterized by (i) cropland, (ii) forests and (iii) urban land use. The performance of the Self-taught learning framework will be assessed and compared to other methods in term of accuracy and computational complexity.