Hyperspectral Image Classification Based on Generative Adversarial Network with Dropblock

Abstract

Deep learning (DL) algorithms are widely applied in hyperspectral images (HSIs) classification. However, the insufficient utilization in spatial semantic information and inadequate number of HSIs samples both restrict the classification performance of DL-based HSIs algorithms. In this paper, we propose a novel method based on generative adversarial network (GAN) with DropBlock structure (DBGAN). Specifically, DropBlock enforces each unit in convolution neural network (CNN) to learn features by dropping contiguous regions of feature maps, therefore more spatial semantic information is capable to contribute in HSIs classification. Furthermore, GAN model can generate realistic samples by an adversarial game to mitigate HSIs data shortage. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.