Research on Hyperspectral Image Classification Based on Multi-task Generative Adversarial Networks

As a prerequisite to realize many applications of hyperspectral images, hyperspectral image classification has been a hot and difficult research area in remote sensing. In recent years, various deep learning network models are in the ascendant and gradually applied to hyperspectral image classification. However, hyperspectral image datasets often have the problems of sparse labeled samples and uneven class distribution, which lead to the overfitting phenomenon of networks when the training samples are limited and achieve low classification accuracy. In order to solve the above problems, this paper deeply investigates generative adversarial networks and improves them, while combining them with multi-task learning to propose hyperspectral classification methods based on multi-task generative adversarial networks. The algorithm achieves deep mining of hyperspectral data features by efficiently training the generator and discriminator, in which the generator produces a large number of virtual samples to expand the training set, and the discriminator relies on the information interaction of the input source discrimination task, the metric task and the ground object classification task to improve the classification performance. The proposed method has validated the effectiveness of each module on two standard datasets, and demonstrated that the method can exhibit relatively excellent classification performance through sample expansion and multi-task learning architecture, with low dependence on the number of training samples.