Dilated convolutional neural network for hyperspectral image feature extraction and classification

Feng-zhe Zhang; Lu Xiao; Hai-bin Wang; Hua-yu Gao; Jun-xiang Wang; Chao Lu

doi:10.1117/12.2558057

3 January 2020 Dilated convolutional neural network for hyperspectral image feature extraction and classification

Feng-zhe Zhang, Lu Xiao, Hai-bin Wang, Hua-yu Gao, Jun-xiang Wang, Chao Lu

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730V (2020) https://doi.org/10.1117/12.2558057
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In this paper, a dilated convolutional neural network is proposed for hyperspectral image classification. Compared with other methods, 2-dimension dilated convolution is used for the first time to extract and classify the spatial-spectral features in hyperspectral image processing fields. Firstly, 1-dimension convolution is extended to 2-dimension convolution for spatial-spectral features extraction. Secondly, a dilated convolutional structure is utilized to fuse the multi-scale information, which is used to extract the multi-scale information without loss of resolution. The experiments of University of Pavia were repeated with the method proposed in this paper, and some better results are obtained, which proved the effectiveness of the proposed model.

Citation Download Citation

Feng-zhe Zhang, Lu Xiao, Hai-bin Wang, Hua-yu Gao, Jun-xiang Wang, and Chao Lu "Dilated convolutional neural network for hyperspectral image feature extraction and classification", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730V (3 January 2020); https://doi.org/10.1117/12.2558057

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KEYWORDS

Convolution

RGB color model

Data modeling

Hyperspectral imaging

Feature extraction

Convolutional neural networks

Neural networks

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