Hyperspectral image vegetation classification method based on spatial auto-correlation information

Abstract

The invention discloses a hyperspectral image vegetation classification method based on spatial auto-correlation information. The method comprises the following steps that firstly a hyperspectral dataset is normalized so that a hyperspectral image data set R of which the information content is redistributed is obtained; then PCA dimension reduction is performed on the hyperspectral images and a new data set K is formed; then spatial information is extracted from all the spectral information R by using domain transformation linear interpolation convolutional filtering; then the spatial information is extracted from the data set K by using domain transformation linear interpolation convolutional filtering; then the extracted spatial information is linearly fused so as to form a new data setH; then a training set Hs is randomly extracted from the spatial information data set H, and the rest part acts as a test set Ht; then the training set HS is trained by using the SVM supported by theradial basis function so as to acquire a training model; and finally the test set Ht is classified by using the SVM supported by the radial basis function so as to obtain the classification result.

Description

technical field [0001] The present invention relates to a hyperspectral image classification method, and more particularly, to a hyperspectral image vegetation classification method based on spatial autocorrelation information. Background technique [0002] In remote sensing hyperspectral, ground features (especially vegetation) are rich in types and have similar spectra. The use of hyperspectral images can effectively classify and identify crops accurately, and can be effectively used in agricultural disaster and yield assessment. Vegetation classification has good unity and strong spatial correlation. [0003] The research on spatial texture information extraction for hyperspectral image classification has achieved certain results, but there are still some shortcomings: firstly, the classification performance of vegetation with similar spectral information needs to be improved; In terms of spatial texture information, the extraction of hyperspectral spatial autocorrelatio...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the defects of low vegetation classification performance and neglect of hyperspectral spatial autocorrelation information in current hyperspectral image classification, and propose a hyperspectral image vegetation classification method based on spatial autocorrelation information

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