Hyperspectral Image Sparse Unmixing Method Based on Neighborhood Spectral Weighting

Abstract

The invention discloses a sparse unmixing method based on neighborhood spectrum weighting for hyperspectral images, mainly aiming to solve the problems of low hyperspectral image sparse unmixing accuracy, poor reconstruction effect, high time consumption and low efficiency in the sparse unmixing process of hyperspectral images with high signal-noise ratios in the prior art. The method comprises the following steps of inputting an unmixing parameter, preprocessing, constructing a fitting sparse matrix, constructing a sparse unmixing model of neighborhood spectrum weighting, solving the spare unmixing model of the neighborhood spectrum weighting and outputting an unmixing result. By introducing a weighting space relevant model and a fitting sparse matrix, the method has the advantages of high sparse unmixing accuracy, good reconstruction effect, low time consumption and high efficiency.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral image sparse unmixing method based on neighborhood spectral weighting in the technical field of sparse unmixing. The invention is used for hyperspectral image unmixing processing of various digital devices, and can effectively improve the precision of hyperspectral image unmixing. Background technique [0002] The hyperspectral image unmixing technology refers to decomposing each mixed pixel in the hyperspectral image into different basic components, or "end members", and obtaining the proportion of these basic components. Among them, "end members" generally refer to the pixels that contain a high proportion of a certain type of ground object extracted from hyperspectral images, not necessarily pure pixels that contain only one type of ground object. [0003] JoseM.Bioucas-Dias and AntonioPlaza proposed in the literature "TotalVariationSpatialRegul...

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, and propose a hyperspectral image sparse unmixing method based on neighborhood spectral weighting to improve the sparse unmixing accuracy of hyperspectral images and overcome the low efficiency of hyperspectral image sparse unmixing The problem of reducing the time-consuming hyperspectral image sparse unmixing

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