Grouped Multi-Attention Network for Hyperspectral Image Spectral-Spatial Classification

Abstract

Deep learning has been a powerful tool for hyperspectral image (HSI) classification. However, it is still an open issue to effectively learn highly discriminative features from the HSI, due to the high-dimensionality and complex spectral-spatial characteristics. To settle this issue, we propose a new band-grouping guided multi-attention module for the performance promotion of spectral-spatial feature learning. First, based on the fact of high relevance between adjacent spectral bands and low dependencies across long-range ones, all the spectral bands are adaptively divided into multiple non-overlapping groups where relevant bands are included. The advantage is to reduce the spectral dimension and data complexity when processing and analyzing each group. Then, a multi-attention mechanism, which not only explore the intra-group salient information but also propagate the inter-group difference information, is embedded into the convolutional neural networks to learn group-specific spectral-spatial features. By emphasizing useful spectral/spatial information and squeezing useless information with attention mechanism, the severability of learned features is enhanced. Based on this module, a spectral-spatial classification network is built, named by grouped multi-attention network (GMA-Net). The GMA-Net contains a two-branch architecture, i.e., pixel-wise spectral feature learning and patch-wise spectral-spatial feature learning. Via fusing the features from two branches, the complementary and discriminative features provided by pixel-wise and patch-wise learning manner can be integrated to further boost classification performance. Experimental results demonstrate that the proposed method is superior than several state-of-the-art approaches. Codes are available at: https://github.com/luting-hnu.