Hyperspectral image classification method and device based on correlation coefficients and joint sparse representation

Abstract

The invention discloses a hyperspectral image classification method and device based on correlation coefficients and joint sparse representation, and belongs to the technical field of image classification. The hyperspectral image classification method comprises the steps: calculating the correlation between two variables through employing the correlation coefficients in combination with the similarity of spectrums and the local space consistency, so as to calculate the correlation between a pixel and each class; calculating a sparsest matrix by using an SOMP algorithm, and calculating a residual error between a pixel and each class by using the matrix; and moreover, constraining the classification function model by using the joint sparse representation and the correlation coefficient, andintroducing a parameter to balance the weights of the joint sparse representation and the correlation coefficient, so that the adaptability and robustness of the model are promoted, and compared withother methods, the hyperspectral image classification method is higher in accuracy and more stable in performance.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to a hyperspectral image classification method and device based on correlation coefficient and joint sparse representation. Background technique [0002] A spectral image with a spectral resolution within the range of 10l is called a hyperspectral image (HyperspectralImage). Through hyperspectral sensors mounted on different space platforms, that is, imaging spectrometers, in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, the target area is simultaneously imaged in tens to hundreds of continuous and subdivided spectral bands . While obtaining surface image information, it also obtains its spectral information, which is the first time that the spectrum and image are truly combined. Compared with multispectral remote sensing images, hyperspectral images have not only greatly improved in terms of information ...

AI Technical Summary

Problems solved by technology

However, most of these methods only consider that pixels from the same class should have similar spectral features, while ignoring the spatial domain of the pixels, and most of these methods only apply a single classification condition, ignoring the fact that two Classification methods are combined and maintain a balance to achieve the possibility of classification

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