Siamese Network Architecture and Graph Convolution-Based Multi-Scale Hyperspectral Image Classification
Extensive work has shown that the hyperspectral image classification method based on the fusion of spectral-spatial information can obtain satisfactory results.It is a challenging problem in the field of remote sensing that how to make full use of the spatial information contained in hyperspectral images and properly integrate it with spectral features to obtain better classification results.In this pa-per,a multi-scale hyperspectral image classification method based on siamese network architecture and graph convolution is proposed.First,the original hyperspectral im-age is divided into three images of the same size but with different spectral features.Then,three images are segmented separately into superpixels using different scales and merged them.The merged superpixels not only greatly reduce the size of the graph and improve the computational efficiency,but also further enhance the role of spatial information in classification.Next,based on the extended siamese network architecture,the main spectral features of the three images are extracted using graph convolution,respectively.Finally,the extracted spectral features are fused using the attention mechanism and input into the fully connected network for classification.Experimental and comparative results on two public datasets,Indian Pines and Sali-nas,show that the proposed method performs better than several competing methods in classification completion.
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