Hyperspectral image sparseness demixing method based on MFOCUSS and low-rank expression

Abstract

The invention discloses a hyperspectral image sparseness demixing method based on multiple focal underdetermined system solver (MFOCUSS) and low-rank expression. The method comprises: original hyperspectral data and known spectrum base data are read; and an objective function of a sparseness de-mixing model based on MFOCUSS and low-rank expression is constructed and the hyperspectral data and known spectrum base are used as input data and a dictionary of the objective function, the objective function of the MFOCUSS and low-rank expression is solved to obtain an abundance matrix of an overall spectrum library, a spectrum of a non-real end member in the spectrum library is rejected, repeated iteration is carried out on the spectrum library after non-real end member rejection to obtain a real end member matrix and a corresponding abundance matrix. According to the invention, direct end member extraction in the original hyperspectral data is avoided; the non-real end member is rejected and the spectrum library is updated; the adverse influence on the hyperspectral de-mixing effect by autocorrelation of the end member spectrum in the spectrum library is reduced; and precision of abundance estimation is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image sparse unmixing method based on Multiple FOCal Underdetermined System Solver (MFOCUSS) and low-rank representation in the technical field of sparse unmixing. Background technique [0002] Hyperspectral remote sensing images are three-dimensional image data composed of dozens or even hundreds of continuous band images, which can organically combine images that determine the spatial characteristics of ground objects with spectral information that determines the properties of ground objects, and can qualitatively and quantitatively analyze the measured objects. Physical analysis and identification, so it is widely used in the fields of land resource utilization, geological exploration, environmental monitoring, marine resource survey, artificial target identification and military detection. However, due to the limitation of optical devices, the pixels in...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, propose a hyperspectral image sparse unmixing method based on MFOCUSS and low-rank representation, reduce the unfavorable effect of the endmember spectral curve in the spectral library on the hyperspectral unmixing effect To improve the accuracy of sparse unmixing of hyperspectral images and overcome the problem of low efficiency of sparse unmixing of hyperspectral images

Images