Graph convolutional network method for small sample classification of hyperspectral images

doi:10.11947/j.AGCS.2021.20200155

Abstract

Abstract: Existing based on convolutional neural network classification method of hyperspectral images usually rules of the square area of image convolution, not widely adapt to different terrain local area distribution and geometry appearance of the image, therefore, under the condition of small sample classification performance is poorer, and figure convolution can network topology information on the map represent irregular image area of the convolution. Therefore, a hyperspectral image classification method based on graph convolution network is proposed. In this method, the spatial spectral information of the image is considered in the process of constructing the graph, and the feature information of the neighbor node is aggregated by the graph convolution network. Experimental results on three data sets, Pavia university, Indian Pines and Salinas, show that this method can achieve a high classification accuracy with a small number of training samples.

Key words: hyperspectral image classification, local binary patterns, graph convolutional network, small sample

CLC Number:

P237

ZUO Xibing, LIU Bing, YU Xuchu, ZHANG Pengqiang, GAO Kuiliang, ZHU Enze. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10): 1358-1369.

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