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Due to its high spectral resolution, Hyperspectral remote sensing data can provide practically continuous spectral curves for target objects and fully reflect the detailed characteristics of ground objects. However, the data redundancy generated by a large number of bands poses challenges to the feature extraction of target objects. Hence, the spectral data are processed by wavelet transform to reduce the influence of intra-class spectral variation on classification. The multi-scale image data were collected by Gaussian pyramid multi-scale transformation. Then the multi-scale spatial information was captured through the feature extraction network to improve the classification accuracy. We propose a dual-branch feature extraction network. The first branch adopts Gaussian pyramid multi-scale transformation to obtain multi-scale images and then applies the feature extraction module to gain multi-scale spatial features. The second branch employs wavelet transform to process spectral data to reduce the impact of abnormal spectral data on classification and then applies a feature extraction module to acquire spectral features. Finally, the spectral and spatial features obtained by the two branches are fused in the full connection layer to achieve an accurate classification. This method can effectively capture the fine features of hyperspectral images by combining spectral features and spatial features of different scales. Simultaneously, it can capture the interaction between spectral and spatial features by combining spatial and spectral features through joint learning. Experimental results on hyperspectral image datasets indicate that the method outperforms other traditional deep learning-based and other advanced classifiers.

Hyperspectral Classification of Two-Branch Joint Networks Based on Gaussian Pyramid Multiscale and Wavelet Transform