A Cross-Scene Self-Representative Network for Hyperspectral Band Selection

This paper proposes a novel deep learning-based framework for hyperspectral band selection, named Cross-Scene Self-Representative Network (CSSRnet). The proposed method leverages the rich labels of the source domain (SD) to guide the band selection in the target domain (TD). To our knowledge, CSSRnet is the first deep learning-based solution for cross-scene hyperspectral band selection. First, the CSSRnet employs contextual attention mechanism to capture the latent features of SD and TD. It combines the self-attention mechanism with convolutional operations to capture static and dynamic contextual information. Then, the self-representative layer provides the self-representative coefficient of SD and TD. Subsequently, the maximum mean difference is utilized to align the self-representative coefficients of both SD and TD. To enhance the representativeness and precision of these coefficients, we introduce different tasks for the SD and TD branches. Finally, a suitable band subset is selected based on a ranking method that evaluates each band's importance by considering its self-representative coefficient matrix. Experiments are carried out to assess the efficacy of CSSRnet. These experiments focus on evaluating classification accuracy across various cross-scene datasets, the utility of cross-scene concepts, and the practical application in coastal wetland. Experimental results confirm the effectiveness of CSSRnet.