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Classification of hyperspectral images by deep learning of spectral-spatial features

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Abstract

Creating accurate land use and land cover maps using remote sensing images is one of the most important applications of remotely sensed data. Abundant spectral information in hyperspectral images (HSI) makes it possible to distinguish materials that would not be distinguishable by multi-spectral sensors. Spectral and spatial information from HSI is of primary importance for image classification. In this study, a hybrid stacked autoencoder (SAE) architecture and support vector machine (SVM) classifier was constructed to classify the HSI. The SAE architecture is constituted by stacking a multiple autoencoder (AE) deep learning network that consists in the encoder and decoder process. Spatial features in a neighbor region extracted from the principal component analysis (PCA) and the texture feature extracted from the gray-level cooccurrence matrix (GLCM) were fed into the classifier. It was found that the best result was from the combination of GLCM texture feature, PCA spatial feature, and spectral feature. Meanwhile, the representative features derived from SAE deep learning network were better than the original features. It reminded us that extracting the representative features from hyperspectral images is a key step of improving classification accuracy.

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Acknowledgments

The authors are very grateful for the comments from the anonymous reviewer to help us improve the quality of this paper.

Funding

This research has been supported by the Natural Science Foundation of China (Grant No. 41571350).

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Authors and Affiliations

  1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Haiyong Ding

  2. School of Geography Science, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Luming Xu & Yue Wu

  3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

    Wenzhong Shi

Authors
  1. Haiyong Ding
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  2. Luming Xu
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  3. Yue Wu
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  4. Wenzhong Shi
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Correspondence to Haiyong Ding.

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Responsible Editor: Biswajeet Pradhan

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Ding, H., Xu, L., Wu, Y. et al. Classification of hyperspectral images by deep learning of spectral-spatial features. Arab J Geosci 13, 464 (2020). https://doi.org/10.1007/s12517-020-05487-4

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  • DOI: https://doi.org/10.1007/s12517-020-05487-4

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