Hyper-spectral image abnormity detection method adopting local self-adaptive threshold segmentation

Abstract

The invention belongs to the field of hyper-spectral image abnormal target detection, and particularly relates a hyper-spectral image abnormity detection method adopting local self-adaptive threshold segmentation. Hyper-spectral data are read in; the hyper-spectral data are processed by adopting a nonlinear KRX operator so that a detection result grayscale image is obtained; the detected grayscale image is preprocessed so that background interference is eliminated; the image (img file='DDA0000624743130000011.TIF' wi='33' he='50' /) is divided into multiple m*n sub-images (img file='DDA0000624743130000013.TIF' wi='63' he='61' /), and a threshold selection is performed on each sub-image via a threshold iterative method; and binarization is performed on the sub-images (img file='DDA0000624743130000012.TIF' wi='39' he='61' /) by using the obtained self-adaptive thresholds Ti, and the whole image is traversed so that a final detection result is obtained. An algorithm aiming at large area of background interference suppression is provided, and background interference is effectively extracted and eliminated by utilizing structural elements of morphology filtering open operation. The local optical threshold is computed by adopting the iterative method without large amount of tests for verifying and obtaining the thresholds so that workload in practical processing can be greatly reduced, and abnormal target detection efficiency and accuracy can be enhanced.

Description

technical field [0001] The invention belongs to the field of hyperspectral image abnormal target detection, and in particular relates to a hyperspectral image abnormal detection method using local self-adaptive threshold segmentation. Background technique [0002] Object detection is an important direction in the application of hyperspectral remote sensing images. According to whether prior knowledge is required, existing target detection techniques can be divided into target detection algorithms that require prior target information or known background and abnormal target detection algorithms that do not require any prior information. Due to the lack of sufficient spectral prior information in many practical processes, hyperspectral image anomaly detection without any prior information is more in line with the needs of practical applications. [0003] The RX operator is one of the most widely used anomaly detection algorithms at present. As a classic algorithm, it is deriv...

AI Technical Summary

Problems solved by technology

In order to accurately detect abnormalities in hyperspectral remote sensing images, and at the same time solve the problem that the global threshold segmentation will fail to detect abnormal objects that only exist in a local range but are submerged in the global background, this invention proposes a local adaptive threshold Segmented hyperspectral anomaly target detection method (Opening-operation local adaptive threshold Kernel RX, OAKRX)

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