Spectral-Spatial Attention Feature Extraction for Hyperspectral Image Classification Based on Generative Adversarial Network

Recent research shows that generative adversarial network (GAN) based deep learning derived frameworks can improve the accuracy of hyperspectral image (HSI) classification on limited labeled samples. However, several studies point out that existing GAN-based methods are heavily affected by the complexity and inefficient description issues of HSIs. The discriminator in GAN always attempts to interpret high-dimensional nonlinear spectral knowledge of HSIs, thus resulting in the Hughes phenomenon. Another critical issue is sample generation. The generator is only used as a regularizer for the discriminator, which seriously restricts the performance for classification. In this article, we propose SSAT-GAN, a semisupervised spectral-spatial attention feature extraction approach based on the GAN that feeds raw data into a deep learning framework, in an end-to-end fashion. First, the unlabeled data is added into the discriminator to alleviate the problems of training samples and supplies a reconstructed real HSI data distribution through adversarial training. Second, to enhance the description of HSIs, we build spectral-spatial attention modules (SSAT) and extend them to the discriminator and the generator to extract discriminative characteristics from abundant spatial contexts and spectral signatures. The SSAT modules learn a three-dimensional filter bank with spectral-spatial attention weights to obtain meaningful feature maps to improve the discrimination of the feature representation. In terms of the mode collapse of GANs, the mean minimization loss is employed for unsupervised learning. Experimental results from three real datasets indicate that SSAT-GAN has certain advantages over the state-of-the-art methods.