# Deep Self-taught Learning for Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/1710.07096 · HTML: https://mlsanity.com/paper/1710.07096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition](https://mlsanity.com/paper/2204.12624/) (2022)

## Abstract (arXiv)

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.