A Multi-Attention Network with Multi-Level Spatial-Spectral Feature Fusion Based on Band Selection for Hyperspectral Image Classification

Hundreds of bands that make up a hyperspectral image (HSI) contain a large amount of redundant information. It is essential to use band selection method to select the effective and representative bands. Besides, the hyperspectral cube typically contains some pixels that do not fall within the same category as the central pixel, which can affect the classification effect. To address above issues, this paper proposes a multi-attention network with multi-level spatial-spectral feature fusion based on band selection (MAFFBS) for HSI classification. The band selection module (BSM) is utilized to choose some bands with substantial amounts of information. Furthermore, we create a new pixel attention block (PAB) to reduce the impact of interfering pixels on the classification. Then, the multi-level spatial-spectral feature fusion block is used to extract and fuse spatial-spectral features at different levels. Finally, the feature reutilization module consisting of the dense block and the spatial attention block is adopted to alleviate the vanishing gradient problem and to produce more discriminative spatial features. Experimental results on two HSI datasets show that the MAFFBS significantly outperforms some state-of-the-art deep learning-based methods.