Self-Supervised Divide-and-Conquer Generative Adversarial Network for Classification of Hyperspectral Images
Jie Feng, Ning Zhao, Ronghua Shang, Xiangrong Zhang, Licheng Jiao
Abstract
Generative adversarial network (GAN) has been rapidly developed because of its powerful generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) easily causes mode collapse in GAN. Moreover, limited training samples in HSIs restrict the generating ability of GAN. These issues may further deteriorate the classification performance of the discriminator. To conquer these issues, a novel self-supervised divide-and-conquer GAN (SDC-GAN) is proposed for HSI classification. In SDC-GAN, a pretext cluster task with an encoder-decoder architecture is designed by leveraging abundant unlabeled samples. By transferring the learned cluster representation from the cluster task, limited labeled samples are divided effectively in the downstream classification. According to the division of clustering, SDC-GAN constructs a generic and several specific branches for both the generator and discriminator. The generator generates all-class and specific-class samples by using the generic and specific branches separately and combines them adaptively. It can weaken the generation preference for the classes with large sample sizes and alleviate the mode collapse problem. Meanwhile, the classification ability of the discriminator is improved by integrating the judgment of specific branches into the generic branch. Experimental results show that SDC-GAN achieves competitive results for HSI classification compared with several state-of-the-art methods.