Method for hyperspectral remote sensing image sparse mix-decomposition based on homogenous indexes

Abstract

A method for hyperspectral remote sensing image sparse mix-decomposition based on homogenous indexes comprises the steps that homogeneity analysis is conducted on each image element of a hyperspectral remote sensing image so that the homogeneity indexes can be extracted; according to a value domain of the homogeneity indexes of the image elements in the whole hyperspectral remote sensing image, linear enhancement is conducted on the homogenous indexes of the image elements, the enhanced homogeneity indexes serve as variable separations based on whole variation to separate a weight of a space regular term in the augmentation Lagrange sparse mix-decomposition algorithm, and a difference quotient operator approaching inter-image-element abundance in the algorithm is redefined. According to the method, the accuracy of sparse mix-decomposition is improved, the space smooth texture of the mix-decomposition abundance approaches the real space smooth texture distribution of the image, and influence on a mix-decomposition result by noise is effectively restrained. According to the method, important application value is brought for the aspects of high-accuracy terrain classification and ground target detection and identification based on the hyperspectral remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a hyperspectral remote sensing image sparse unmixing method based on a homogeneity index. Background technique [0002] Hyperspectral remote sensing images have high spectral resolution and can analyze the material composition of ground objects more carefully and accurately, so they have been widely used. However, the spatial resolution of hyperspectral images is generally low, and mixed pixels are ubiquitous, which greatly hinders its application. Therefore, the decomposition of mixed pixels has become a key technology to promote the breakthrough of its application. The process of mixed pixel decomposition is to identify different types of ground objects (endmembers) from mixed pixels, and calculate their proportion (abundance) in mixed pixels, which is the core problem of hyperspectral remote sensing image analysis one. [0003] The mixed pixel decomposi...

AI Technical Summary

Problems solved by technology

These methods build consistent spatial smoothness models for different spatial neighborhoods; however, the spatial distribution of endmember abundance is extremely complex and the smoothness is not consistent

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