Deep learning technology provides technical means for hyperspectral image classification due to its unique advantages in deep mining of features. However, in the pixel-level feature classification of hyperspectral images, the number of deep learning layers is limited due to the influence of the sample input size, and the depth features in the hyperspectral images cannot be fully mined. The classification of hyperspectral image based on feature fusion of residual network is proposed in this paper. First, the principal component analysis (PCA) method is used to extract the first principal component in the original hyperspectral image, and the residual network is used to effectively extract the spatial spectrum features of the ground objects; then the feature map is expanded by the deconvolution algorithm, and after deconvolution, features of different dimensions are fused with multi-scale features to fully mine the depth feature information in the hyperspectral image, thus further improving the classification accuracy of the hyperspectral image. The ground feature classification experiment was conducted on the two areas of Taihu Lake in Jiangsu and Chaohu Lake in Anhui captured by the “Zhuhai-1” satellite. The results show that, compared with other methods, this method can effectively solve the problem of insufficient depth feature extraction in hyperspectral image classification, thus showing better classification performance.
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HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
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