Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 57, Issue 9, Pages 6808-6820Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2908756
Keywords
Convolutional neural network (CNN); feature learning; hyperspectral; spatial-spectral
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Funding
- National Natural Science Foundation of China [61671383, 61301235]
- Fundamental Research Funds for the Central Universities [3102018AX001]
- Natural Science Foundation of Shaanxi Province [2018JM6005]
- China Postdoctoral Science Foundation [2014M550872]
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Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial-spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial-spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D-CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D-CAE is very effective in extracting spatial-spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application.
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