SCASAN: Spatial Central Attention-Spectral Attention Network Based Feature Extractor for Hyperspectral Image Classification

Abstract

Due to the influence of space, time, environmental changes, and types of ground objects on the lighting conditions of high-light falling images, Hyperspectral image (HSI) classification is challenging. Recently, attention-based HSI classification has received increasing attention. It uses both spatial attention and spectral attention to capture the required spatial-spectral features, thereby achieving better classification accuracy. However, it is challenging to connect the two attention mechanisms without compromising the spatial-spectral features. In addition, how to obtain the attention weight matrix which determines the importance of each channel or spatial location is also an important problem in the use of attention mechanisms. In response to the above issues, the Spatial Central Attention-Spectral Attention network (SCASAN) is designed to efficiently model the spatial-spectral information for HSI classification. In this network, we propose the novel spectral weight spatial attention (SWSA) module to solve the connection problem between spatial attention and spectral attention. Furthermore, we utilize a spatial central attention network (spatial CAN) to obtain a spatial attention weight matrix to avoid the impact of non-homogeneous adjacent pixels on the classified pixel. Our proposed approach demonstrates superior performance in hyperspectral image classification compared to state-of-the-art methods, as evidenced by comprehensive experiments conducted on three publicly available datasets.