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Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks

Generative adversarial networks (GANs) are famous for generating samples by training a generator and a discriminator via an adversarial procedure. For hyperspectral image classification, the collection of samples is always difficult. However, directly applying GAN to hyperspectral image classification exists two problems. One is that the generated samples lack discriminative information. Meanwhile, the discriminator has no discriminative ability for multiclassification. Another is that spatial and spectral information requires to be considered in hyperspectral image classification simultaneously. To address these problems, a novel multiclass spatial–spectral GAN (MSGAN) method is proposed. In MSGAN, two generators are devised to generate the samples containing spatial and spectral information, respectively, and the discriminator is devised to extract joint spatial–spectral features and output multiclass probabilities. Moreover, novel adversarial objectives for multiclass are defined. The discriminator is devised to predict training samples belonging to true classes and generated samples belonging to all the classes with the same probability. The generators are devised to make the discriminator mistake. By adversarial learning between the discriminator and generators, the classification performance of the discriminator is promoted with the assistance of discriminative generated samples. Experimental results on hyperspectral images demonstrate that the proposed method achieves encouraging classification performance compared with several state-of-the-art methods, especially with the limited training samples.