Abnormal detection method and device for hyper-spectral image

Abstract

The invention provides an abnormal detection method and device for a hyper-spectral image. The method includes the steps that the hyper-spectral image to be detected is obtained, abnormal detection is performed on the hyper-spectral image to be detected through an RX algorithm, so that an abnormal detection result is obtained, threshold segmentation is performed on the abnormal detection result to obtain a target image element set and a background image element set, the mean vector and the covariance matrix of the target image element set are calculated, the mean vector and the covariance matrix of the background image element set are calculated, the distance between each image element to be detected in the hyper-spectral image and each background image element is calculated through the mean vector and the covariance matrix of the background image element set, the distance between each image element to be detected in the hyper-spectral image and each target image element is calculated through the mean vector and the covariance matrix of the target image element set, and the distance between each image element to be detected and each background image element is subtracted from the distance between each image element to be detected and each target image element to obtain the abnormal detection result. The method and device can improve the detection rate of abnormal objects and reduce the false alarm rate.

Description

technical field [0001] The invention relates to the technical field of anomaly detection, in particular to a hyperspectral image anomaly detection method and device. Background technique [0002] Anomaly detection is a hot issue in the hyperspectral field. Anomaly targets refer to features that people are interested in. It has two characteristics. First, it has obvious spectral differences. Second, it has a low probability of appearing in the image. [0003] In the prior art, the RX algorithm is usually used to implement anomaly detection. The RX algorithm assumes that the image background obeys multivariate normal distribution, and describes the background information by calculating the mean vector and covariance matrix of the background pixel set. In the process of realizing the invention, the inventor found that the RX algorithm cannot accurately estimate the background information, resulting in a high false alarm rate in the detection results. Contents of the invent...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the present invention provides a hyperspectral image abnormality detection method and device to solve the problem in the prior art ...

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