An Optimized Training Method for GAN-Based Hyperspectral Image Classification

Abstract

This letter explores how to apply a generative adversarial network (GAN) to the classification of hyperspectral images (HSIs) to obtain a smooth training process and better classification results. To this end, the ideas of the progressive growing GAN (PG-GAN) and Wasserstein generative adversarial network gradient penalty (WGAN-GP) are combined to propose a new method for HSI classification. PG-GAN is optimized from the training process of generating adversarial networks. It gradually increases the depth of the network and the size of the input image, making the training smoother. WGAN-GP is optimized in terms of the loss function. The gradient penalty method is used to solve the problems of vanishing gradient and exploding gradient, making the training more stable. Based on the combination of the two methods, a classifier is added to the model so that it can complete the HSI classification task. The proposed method is evaluated over two publicly available hyperspectral data sets, the Indian Pines and University of Pavia data sets. The results show that the proposed method can achieve good training results with only a small amount of labeled training data.