Abstract
Sample generation is an effective way to solve the problem of the insufficiency of training data for hyperspectral image classification. The generative adversarial network (GAN) is one of the popular deep learning methods, which utilizes adversarial training to generate the region of samples based on the required class label. In this paper, we propose cascade conditional generative adversarial nets for hyperspectral image complete spatial-spectral sample generation, named C2GAN. The C2GAN includes two stages. The stage-one model consists of the spatial information generation with a window size that entails feeding random noise and the required class label. The second stage is the spatial-spectral information generation that generates spectral information of all bands in the spatial region by feeding the label regions. The visualization and verification of generated samples based on the Pavia University and Salinas datasets show superior performance, which demonstrates that our method is useful for hyperspectral image classification.
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Acknowledgements
This work was supported by National Nature Science Foundation of China (Grant Nos. 61973285, 61873249, 61773355, 61603355), National Nature Science Foundation of Hubei Province (Grant No. 2018CFB528), Opening Fund of the Ministry of Education Key Laboratory of Geological Survey and Evaluation (Grant No. CUG2019ZR10), and Fundamental Research Funds for the Central Universities (Grant No. CUGL17022).
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Liu, X., Qiao, Y., Xiong, Y. et al. Cascade conditional generative adversarial nets for spatial-spectral hyperspectral sample generation. Sci. China Inf. Sci. 63, 140306 (2020). https://doi.org/10.1007/s11432-019-2798-9
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DOI: https://doi.org/10.1007/s11432-019-2798-9