Deep Self-taught Learning for Remote Sensing Image Classification

Anika Bettge; Ribana Roscher; Susanne Wenzel

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arXiv · 1710.07096

Deep Self-taught Learning for Remote Sensing Image Classification

Anika Bettge·Ribana Roscher·Susanne Wenzel

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Abstract

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.

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BibTeX RIS

Anika Bettge, Ribana Roscher, Susanne Wenzel. 2017-10-19. Deep Self-taught Learning for Remote Sensing Image Classification. https://arxiv.org/abs/1710.07096

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