High-spectrum quick abnormity detection method adopting kernel recursion

Abstract

The invention belongs to image processing, and relates to the field of high-spectrum image abnormity target detection, in particular to a high-spectrum quick abnormity detection method adopting kernel recursion. The method includes the steps that high-spectrum data are read in, a kernel matrix of background information is initialized, a causal relationship of background pixel updating is built, a recursion equation of the kernel matrix KB(n) is built to update the kernel matrix, and a high-spectrum image is quickly detected through a KRX operator. An updating model adopting background information causality is provided, the background information is quickly updated through changes of detected pixels, and data redundancy caused by repeatedly extracting the background information is avoided. A brand new kernel matrix is built, so that the problem that dimension of the kernel matrix changes along with increasing of the number of the background pixels is effectively solved, and meanwhile, the kernel matrix does not need to be calculated every time in an algorithm. The recursion concept of the Kalman filter theory is introduced, workload in actual processing can be greatly reduced, and abnormal target detection efficiency is improved.

Description

technical field [0001] The invention belongs to the field of image processing, especially hyperspectral image abnormal target detection, and specifically relates to a hyperspectral fast abnormal detection method using kernel recursion. Background technique [0002] Hyperspectral remote sensing images are widely used in object classification, target recognition and detection because of their high spectral resolution. effective distinction between true and false targets. For target detection, due to the wide variety of actual ground objects, there is currently no complete spectral database to provide the required prior information, making the acquisition of prior spectral information a difficult point in target detection, so prior information is not required The hyperspectral image anomaly detection algorithm has become a hot research direction. [0003] The RX anomaly detection algorithm first proposed by Reed and Xiao Li uses the difference in statistical characteristics b...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a hyperspectral fast anomaly detection method using kernel recursion that improves the detection speed and solves the problem of data storage

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