Hyperspectral abnormal object detection method based on structure sparse representation and internal cluster filtering

Abstract

The invention discloses a hyperspectral abnormal object detection method based on structure sparse representation and internal cluster filtering, aiming at addressing the technical problem of low object detection effciency of current hyperspectral abnormal object detection methods. The technical solution involves: after selecting an initial background pixel, using the dictionary learning method which is based on principal component analysis to study a background dictionary which obtains rebustness, in the course of sparse vector resolution and image reconstruction, introducing re-weighted laplacian prior to increase the solution precision of sparse vector, computing the errors betwen an original image and a reconstructed image to obtain a sparse representation error, using the internal cluster filtering to represent space spectrum characteristics of hyperspectral data, obtaining the internal cluster error by computing the error between a to-be-tested pixel and other pixel linear representation result, and finally combining the sparse representation error and the linear weighting of the internal cluster error and implementing precise extraction of an abnormal object. According to the invention, the method increases 10-15% of detection rate with the proviso of a constant false alarm rate compared with prior art.

Description

technical field [0001] The invention relates to a hyperspectral abnormal target detection method, in particular to a hyperspectral abnormal target detection method based on structural sparse representation and internal clustering filtering. Background technique [0002] Hyperspectral anomaly target detection technology is a hyperspectral target detection technology that only uses the spectral difference between image pixels to detect targets without providing prior spectral information of the target to be measured. Strong practicality. [0003] Compared with the entire hyperspectral image, the abnormal target not only has a lower probability of occurrence, but also accounts for a small proportion. Traditional abnormal target detection algorithms generally assume that the image background obeys a Gaussian distribution. Under this assumption, global or local statistical properties can be used to detect abnormal targets. However, in practical applications, due to the limitati...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of low target detection efficiency of the existing hyperspectral anomaly target detection methods, the present invention provides a hyperspectral anomaly target detection method based on structural sparse representation and internal clustering filtering

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