A hyperspectral anomaly detection method based on an adversarial self-coding network

Abstract

The invention discloses a hyperspectral image anomaly detection method based on an adversarial self-coding network, and mainly solves the problems of complex calculation and low detection precision inthe prior art. The implementation scheme comprises the following steps of: 1) manufacturing a hyperspectral image training data set by using a pixel updating method; 2) inputting the training data set into a generative adversarial network for training, and extracting spectral characteristics of the training data set; 3) processing the spectral features by using a waveband fusion and attribute filtering method to obtain spatial features of the training data set; 4) enhancing an abnormal target in the original hyperspectral image by utilizing spatial characteristics; 5) calculating an abnormalvalue of the hyperspectral image spectral vector after the abnormal target is enhanced by using an RX detector formula; According to the method, richer potential information in the hyperspectral imagecan be obtained, the difference between an abnormal target and a complex background in the image is increased, the method has the advantages of being simple in calculation and high in detection precision, and the method can be used for detecting the abnormal target in the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral anomaly detection method, which can be used to detect abnormal targets in hyperspectral images. Background technique [0002] Hyperspectral images have rich spectral information and high spectral resolution, and have certain research value in the fields of target detection, classification, and recognition. Hyperspectral image anomaly detection is an unsupervised target detection method. When the prior information of the target and the background is unknown, the method judges whether it belongs to the abnormal point or the background target by comparing the difference between the detected point and the selected background spectral curve. Usually, prior knowledge of objects and backgrounds is difficult to obtain, so hyperspectral anomaly detection techniques have greater significance in practical applications. [0003] The classic anomaly detection a...

AI Technical Summary

Problems solved by technology

Although this method can reduce the computational complexity, it only considers the spectral features of the hyperspectral image and ignores the spatial features, so the detection accuracy is not high

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