Immune Evolutionary Generative Adversarial Networks for Hyperspectral Image Classification

In recent years, hyperspectral image classification (HIC) algorithm based on deep learning has been widely studied, and has achieved much better results than traditional algorithms. HIC using small samples has gradually become a research hotspot, and the generative adversarial networks (GANs) have become a brilliant application in this field. However, the HIC results based on GAN methods are poor and volatile, since a single loss function cannot accurately measure the distance between the generated samples and the real samples in different hyperspectral images. To resolve this problem, we propose a novel immune evolutionary generative adversarial network (HIEGAN) via leveraging the evolutionary strategy and immune strategy. Specifically, we enhance the performance of the generator in two ways: 1) HIEGAN uses multiple loss functions for calculation and backpropagation, so as to endow the generator with different parameter values and select the best one as the evolution result each time to enter the next iteration and 2) in the training process, we preserve the optimal generator as memory cells to avoid the performance degradation of the generator. Through these changes, HIEGAN overcame the defects of GAN, improved the stability of GAN, and finally improved classification efficiency. At the same time, in order to alleviate the overfitting problem of depth network under small samples, we change convolution and deconvolution into ghost module to reduce the network parameters. Experiments on three classical datasets validate that HIEGAN has encouraging performance in HIC under small samples.