Principal component analysis to filter out most of the redundant information in the n-dimensional hyperspectral remote sensing image is used, and the m-dimensional hyperspectral remote sensing image that retains the spectral information is obtained as much as possible, and fuses its spatial distribution information, each pixel in the m-dimensional hyperspectral remote sensing image is constructed as a 2-dimensional spectrum-space information vector. The principal component analysis method is used to reduce the dimension of the m-dimensional hyperspectral remote sensing image again, and the result map of the q-dimensional fusion of the spatial distribution information of the ground features and the spectral information is obtained. The Gaussian mixture model is used to predict the clustering center, and the hyperspectral remote sensing image is clustered based on the improved ISODATA (Iterative Selforganizing Data Analysis Techniques Algorithm) to obtain the final classification result. Experimental results show that the classification accuracy of the proposed method is better than that of K-means, ISODATA and SVM, the overall classification accuracy is increased by 10.14%-13.99%, and the kappa coefficient is increased by 3.2%-12.85%.
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