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Residual deep PCA-based feature extraction for hyperspectral image classification

  • Extreme Learning Machine and Deep Learning Networks
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Abstract

In hyperspectral image (HSI) classification, a big challenge is the limited sample size with a relatively high feature dimension. Therefore, effective feature extraction of data is essential, which is desired to remove the redundancy as well as improve the discrimination. A huge number of methods have been proposed for HSI feature extraction. In recent years, deep learning-based feature extraction algorithms have shown their superiorities in various classification problems. Within them, deep PCA (DPCA) is a simple but efficient algorithm, which runs fast due to the absence of back-propagation. However, DPCA fails to provide satisfactory classification accuracies on HSI datasets. In this paper, we try to combine DPCA with residual-based multi-scale feature extraction and propose a residual deep PCA (RDPCA) feature extraction algorithm for HSI classification. It is a hierarchical approach consisting of multiple layers. Within each layer, PCA is utilized for layer-wise feature extraction, and the reconstruction residual is fed into the next layer. When the feature is passed deeper into the RDPCA network, finer details are mined. The layer-wise features are concatenated to form the final output feature. Furthermore, to enhance the ability of nonlinear feature extraction, we add activation functions between adjacent layers. Experimental results on real-world HSI datasets have shown the superiority of the proposed RDPCA over DPCA and PCA.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant Nos. 61701468, 61171151, 1571393 and 61272315) and International Cooperation Project of Zhejiang Provincial Science and Technology Department (Grant No. 2017C34003).

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Authors and Affiliations

  1. College of Information Engineering, China Jiliang University, Hangzhou, China

    Minchao Ye, Chenxi Ji, Hong Chen, Ling Lei & Huijuan Lu

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yuntao Qian

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  1. Minchao Ye
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  2. Chenxi Ji
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  3. Hong Chen
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Correspondence to Huijuan Lu.

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Ye, M., Ji, C., Chen, H. et al. Residual deep PCA-based feature extraction for hyperspectral image classification. Neural Comput & Applic 32, 14287–14300 (2020). https://doi.org/10.1007/s00521-019-04503-3

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