Generative Adversarial Network-Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

Jiaxin Bai, Erlei Zhang, Lei Yang, Xinyu Li, Shuyin Zhang

Abstract: Classification of hyperspectral images (HSIs) is a crucial topic in the domain of remote sensing. However, existing HSI classification methods often fail to adequately consider the connection between mid-level and high-level spectral-spatial features. Consequently, we propose a novel method named generative adversarial network-based spectral–spatial learning (GAN-SSL) for HSI classification. The method leverages the high spatial resolution of panchromatic (PAN) images and combines PAN images with HSIs to generate higher-quality HSIs, resulting in improved classification accuracy. Firstly, the dual-stream GAN is employed to generate higher-quality HSI from both HSI and PAN images. Secondly, the shallow-deep feature extraction classification network is utilized to capture both mid-level and high-level spectral–spatial information from the obtained features and a classifier is employed to categorize the extracted information. The experimental results obtained from two datasets indicate that the proposed approach surpasses several state-of-the-art techniques in terms of classification performance.