  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.
