Dynamic spectral–spatial multiscale feature extraction network for hyperspectral image classification

Qi Yang; Xueying Cao

doi:10.1117/12.2643236

12 October 2022 Dynamic spectral–spatial multiscale feature extraction network for hyperspectral image classification

Qi Yang, Xueying Cao

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234207 (2022) https://doi.org/10.1117/12.2643236
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Aiming at the problem of insufficient spectral–spatial feature extraction under the condition of limited labeled samples in hyperspectral image classification task, in this paper, a dynamic spectral–spatial multiscale feature extraction network is proposed to extracting more discriminative feature information. Different from the fixed size kernels to extract single feature information, we add dilated convolution in multiscale convolution network to obtain the fusion of neighborhood and global feature information. Besides, a joint spectral–spatial dynamic convolution network is proposed constructed with double attention branches. Spectral attention module is introduced in dynamic convolution to adaptively enhance the useful bands in classification task. That makes dynamic convolution neural network more effective through reconstructing the feature information obtained from different kernels. The experiments, conducted on two commonly hyperspectral image datasets, demonstrate that the proposed method is superior to other state of art classification methods.

Citation Download Citation

Qi Yang and Xueying Cao "Dynamic spectral–spatial multiscale feature extraction network for hyperspectral image classification", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234207 (12 October 2022); https://doi.org/10.1117/12.2643236

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KEYWORDS

Convolution

Feature extraction

Hyperspectral imaging

Image classification

Neural networks

Principal component analysis

Convolutional neural networks

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