Hyperspectral image abnormity detection method based on constrained sparse representation

Abstract

The invention belongs to the field of image processing, and relates to a hyperspectral image abnormity detection method based on constrained sparse representation. The method comprises the following steps: (S1), carrying out the linear standardization of a hyperspectral image; (S2), extracting a local background dictionary for each test pixel according to a double-window model; (S3), solving a constrained sparse representation model according to the local background dictionary, and obtaining a first model optimal solution; (S4), deleting all abnormal atoms from the local background dictionaryaccording to the first model optimal solution, and obtaining a new background dictionary; (S5), solving the constrained sparse representation model according to the new background dictionary, and obtaining a second model optimal solution; (S6), calculating a detection value of the pixel according to the second model optimal solution; (S7), traversing the whole hyperspectral image, calculating thedetection values of all pixels of the hyperspectral image, and outputting an image formed by the detection values, i.e., an abnormal detection image. The method does not need the background statistical information and the setting of sparsity, and improves the reconstruction precision.

Description

Technical field [0001] The invention belongs to the field of image processing and relates to a hyperspectral image abnormality detection method based on constrained sparse representation. Background technique [0002] Hyperspectral image target detection is one of the important directions of hyperspectral remote sensing applications, covering many fields such as environmental detection, urban survey, mineral mapping and military reconnaissance. Anomaly detection does not require any target spectral information, and has a broader application prospect. In anomaly detection, anomalous points are often detected by testing the significant difference between the pixel spectrum and its local background spectrum. [0003] The classic anomaly detection algorithms include Reed-Xiaoli (RX) detector, nuclear RX detector, support vector data description (SVDD) detector and so on. However, when the target signal is contained in the local background, the performance of the traditional anomaly d...

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies of the existing technology, in order to solve the above-mentioned problem that the local background is polluted by the target signal, and the problem that the degree of sparsity in the sparse representation is difficult to determine, the specific technical solutions adopted by the present invention are as follows

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