Hyperspectral image classification method for spatial feature adaptive optimization

Abstract

The invention discloses a hyperspectral image vegetation classification method for spatial feature adaptive optimization. The method comprises the steps that first, normalization processing is performed on a hyperspectral image, PCA dimension reduction is performed on the hyperspectral image obtained after normalization processing, morphological filtering is performed on the hyperspectral image obtained after PCA dimension reduction, and a spatial information set is acquired; second, domain transformation standard convolution filtering is performed on the hyperspectral image obtained after normalization processing for spatial information extraction, and spatial information linear superposition fusion is performed on the acquired spatial information set; and finally the spatial informationset obtained after fusion is classified. Through the method, spatial texture information and relevancy information are effectively utilized, filtering parameters of a best classified structural type is further optimized, a good filtering effect is achieved, and classification precision of the hyperspectral image is effectively improved.

Description

technical field [0001] The present invention relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method for adaptive optimization of spatial features. Background technique [0002] Hyperspectral imaging technology has high spectral resolution. Hyperspectral remote sensing images obtained by imaging spectrometers can reach spectral information of hundreds of bands, and can obtain more comprehensive and spectral features of ground objects, thereby greatly improving the classification of ground objects. degree and accuracy. Using morphological filtering to extract spatial information and combining space and spectrum to improve the classification performance of hyperspectral images is a research hotspot at present. Some scholars use various morphological feature extraction methods and implement classification by SVM. [0003] Morphological filtering has achieved certain results in the research of hyperspectral image spat...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the defects that the current morphological filtering cannot adapt to different data sets and easily lose spatial correlation information, and propose a hyperspectral image classification method for adaptive optimization of spatial features

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