Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

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discriminative spectral and spatial stateoftheart method hyperspectral image hsi classification features doublebranch multiattention mechanism network attention mechanism hyperspectral datasets

Wenping Ma,Qifan Yang,Yue Wu,Wei Zhao,Xiangrong Zhang,.Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification. 11 (11),.

Wenping Ma,Qifan Yang,Yue Wu,Wei Zhao,Xiangrong Zhang,"Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification" 11

Wenping Ma,Qifan Yang,Yue Wu,Wei Zhao,Xiangrong Zhang,"Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification"