Low-rank sparse decomposition hyperspectral anomaly detection method based on local features

Abstract

In order to improve the precision of hyperspectral target detection, the invention provides a low-rank sparse matrix decomposition hyperspectral abnormal target detection method based on local features in allusion to the lack of low-rank information based on the local features in hyperspectral abnormal target detection. According to the method, on the basis of a traditional low-rank sparse matrixdecomposition method, the background part of a hyperspectral image is further refined to represent the product of a basis matrix B and a coefficient matrix C according to the low-rank property of thebackground of the hyperspectral image and the sparsity of an abnormal target, and a hyperspectral image description model based on local features is constructed; secondly, iterative updating rules ofthe new basis matrix B, the coefficient matrix C and the sparse part S are constructed; and finally, abnormal target detection is performed according to a solving result. Experiments prove that the method can improve the hyperspectral abnormal target detection precision.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a low-rank sparse decomposition hyperspectral anomaly detection method based on local features. Background technique [0002] Hyperspectral images have high spectral resolution and contain rich and detailed spectral information of ground objects, which greatly improves the ability to distinguish ground objects. In the field of target detection, hyperspectral images can use the unique spectral curve characteristics of various ground objects to identify many similar ground objects that cannot be identified by multispectral images. The research on hyperspectral target detection algorithm has always occupied an important position in the field of hyperspectral remote sensing data analysis and processing. [0003] When the target spectral information is difficult to obtain, abnormal target detection methods are often used to detect abnormal targets....

AI Technical Summary

Problems solved by technology

However, the traditional methods based on low-rank sparse matrix factorization often focus on matrix factorization, ignoring the internal relationship based on local features, resulting in insufficient precision of low-rank sparse matrix factorization, which affects the detection accuracy of abnormal targets.

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