Hyperspectral Image Classification Based on Wavelet Convolution Network

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    Hyperspectral images have multiple bands and a strong correlation between bands, but their spatial texture and geometric information are poorly expressed. The traditional classification model has insufficient extraction of spatial spectral features and large calculation, and its classification performance needs to be improved. To solve this problem, a multi-scale and multi-resolution attention feature fusion convolution network (WTCAN) based on the wavelet transform is proposed. The concept of wavelet transform is applied to decompose the spectral band four times, and the hierarchical extraction of spectral features can reduce the calculation amount. The network has designed the spatial information extraction module and introduced the pyramid attention mechanism. By designing the reverse jump connection network structure, it uses multiple scales to obtain the spatial position features and enhances the expression ability of spatial texture, which can effectively improve the defects of traditional 2D-CNN feature extraction, such as single scale and the ignoring of spatial texture details. The proposed WTCAN model is experimented on the hyperspectral datasets with different spatial resolutions—Indian Pines (IP), WHU_Hi_HanChuan (HanChuan), and WHU_ Hi_ HongHu (HongHu) repectively. By comparing the effects of SVM, 2D-CNN, DBMA, DBDA, and HybridSN models, the WTCAN model achieves excellent classification results. The overall classification precision of the three datasets reaches 98.41%, 99.64%, and 99.67% respectively, which can provide a valuable reference for the research on the classification of hyperspectral images.

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  • Received:January 05,2023
  • Revised:February 09,2023
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  • Online: May 24,2023
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