High-spectrum quick abnormity detection method adopting kernel recursion

An anomaly detection, hyperspectral technology, applied in the field of image processing

Active Publication Date: 2015-07-08
HARBIN ENG UNIV
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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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  • High-spectrum quick abnormity detection method adopting kernel recursion
  • High-spectrum quick abnormity detection method adopting kernel recursion
  • High-spectrum quick abnormity detection method adopting kernel recursion

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Embodiment Construction

[0038] The concrete realization of the present invention is described in more detail below in conjunction with accompanying drawing:

[0039] The present invention aims at the problem of slow processing speed of hyperspectral data in the existing hyperspectral anomaly detection traditional kernel method, and first uses all pixels before the current detection pixel as background information to replace the traditional local concentric double-layer window , establish the causal relationship of background pixel update, and then introduce the idea of ​​Kalman filter theory to establish the recursive update equation of the kernel matrix, so that there is no need to repeatedly calculate the kernel matrix that maps the high-dimensional feature space during processing, reducing the workload of the algorithm in actual processing , thus implementing a fast anomaly detection algorithm for hyperspectral images. This method uses recursion to update the kernel matrix of the background inform...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 赵春晖王佳王玉磊肖健钰尤伟
Owner HARBIN ENG UNIV
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