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Hyperspectral image classification based on three branch network with grouped spatial-spectral attention

  • role： First author第一作者
  • Affiliation：

    Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China

  • Email：suhan@stu.hubu.edu.cn
  • Introduction：苏涵，研究方向为图像处理。E-mail： suhan@stu.hubu.edu.cn
SU Han1,  
  • Affiliation：

    Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China

CHEN Na1,  
  • Affiliation：

    Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China

    Industrial Research Institute of Remote Sensing, Ningbo University, Shiye Intelligent Technology Co., Ltd, Ningbo 315211, China

PENG Jiangtao13,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Industrial Research Institute of Remote Sensing, Ningbo University, Shiye Intelligent Technology Co., Ltd, Ningbo 315211, China

  • Email：sunweiwei@nbu.edu.cn
  • Introduction：孙伟伟，研究方向为多源遥感协同智能解译与海岸带健康监测。E-mail： sunweiwei@nbu.edu.cn
SUN Weiwei23*

Resümee

Hyperspectral images have abundant spatial and spectral information. Numerous hyperspectral classification algorithms focus on the extraction and maximization of spatial and spectral information. Deep feature extraction networks generally extract spectral-spatial features using single-branch serial or double-branch parallel structures. However, single-branch structures may lead to mutual interference between features of spectral and spatial dimensions, and double-branch parallel structures tend to ignore the correlation between spatial and spectral features. This paper proposes a three-branch grouped spatial-spectral attention network (TGSSAN) to consider the differences and correlations between spatial and spectral features. TGSSAN can extract independent spectral-spatial features while preserving their correlation.This paper proposes the TGSSAN, which has three parallel branches (i.e., spectral, spatial, and spectral-spatial branches). These branches can separately extract spectral, spatial, and spatial-spectral features. Different attention blocks are designed in three branches to enhance the discriminative capability of features. In particular, a grouped spatial-spectral attention mechanism is proposed in the spectral-spatial branch to obtain spatial and spectral attention simultaneously. Finally, three branch features are fused for classification.In the experiment, the proposed TGSSAN algorithm is compared with some advanced deep learning algorithms, such as SSRN, FDSSC, DBMA, DBDA, HResNetAM, and A2S2KResNet. The performance of different algorithms is evaluated on five hyperspectral data sets. Experimental results show that the proposed algorithm achieved superior classification performance on IP, PU, SA, HU, and HHK datasets. In particular, the proposed algorithm achieves higher classification accuracy despite limited training samples compared with the existing advanced algorithms.The TGSSAN method proposed in this paper improves the shortcomings of the single-branch serial and double-branch parallel structures for continuous extraction of spectral-spatial features, which can effectively extract image spectral-spatial feature information. The three attention blocks designed in this paper namely, spectral, grouped spatial-spectral, and spatial attention modules, can effectively enhance the feature discrimination capability and further improve the classification performance.

Schlüsselwort

hyperspectral image classification;attention mechanism;three-branch network;deep network

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