Generative adversarial networks and spatial uncertainty sample selection strategy for hyperspectral image classification

Wenyue Yu; Sijie Niu; Xizhan Gao; Kun Liu; Jiwen Dong

doi:10.1117/12.2680555

27 June 2023 Generative adversarial networks and spatial uncertainty sample selection strategy for hyperspectral image classification

Wenyue Yu, Sijie Niu, Xizhan Gao, Kun Liu, Jiwen Dong

Author Affiliations +

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051A (2023) https://doi.org/10.1117/12.2680555
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Hyperspectral image classification is widely used in agriculture, atmospheric environment and other fields. In recent years, deep learning has achieved remarkable success in hyperspectral image classification. However, supervised deep learning largely depends on training sets with high-quality labels, and obtaining large-scale data with high-quality labels is difficult, expensive and time-consuming. Therefore, in response to the problem of insufficient training samples, this paper proposes a hyperspectral image classification method based on generative adversarial networks and spatially uncertain sample selection strategy. By designing two generation networks composed of Autoencoder, the real spectral bands and spatial patches are input into the generation network to generate spectral and spatial information respectively. In order to extract more discriminative features, the discriminator uses different convolution kernels to fuse features and extract joint spatial spectral features. In addition, this paper adopts spatial uncertainty sample selection strategy, which selects more representative and informative samples for labeling. The network designed in this paper is combined with the sample selection strategy to further improve the recognition ability of the discriminator. Experimental results on three hyperspectral image datasets show that compared with several existing methods, this method is less sensitive to the number of training samples and has higher classification performance in the case of limited training samples.

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Wenyue Yu, Sijie Niu, Xizhan Gao, Kun Liu, and Jiwen Dong "Generative adversarial networks and spatial uncertainty sample selection strategy for hyperspectral image classification", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051A (27 June 2023); https://doi.org/10.1117/12.2680555

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KEYWORDS

Hyperspectral imaging

Education and training

Image classification

Feature extraction

Visualization

Deep learning

Image fusion

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