Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection

Abstract

A new algorithm for hyperspectral image anomaly detection is proposed by designing an adaptive multi-layer structure with spatial-spectral combination information, which is different from the traditional anomaly detection algorithms only considering the spectral difference between the anomaly point and the background pixels, and ignoring the difference between the local spatial structure and spectrum. Firstly, the present algorithm not only calculates the spectral dimension difference between the pixels to be measured and the pixels in the background window, but also measures the spatial structure difference between the internal window and the background window. Mostly, an adaptive multi-layer structure for anomaly detection framework is carried out based on the idea of background suppression, and a multi-layered anomaly detector is constructed. The anomaly detection results of each layer of the detector are taken as the constraints, and the background information of the image input in the detector of the next layer is suppressed, adaptively suppressing the background noises. The experimental results show that the present algorithm makes better use of both the local spatial structure and the spectral dimension information than the traditional two-window models (global RX, local RX and KRX), adaptively suppresses background, reduces the false alarm rate, and improves the detection effect of the abnormal targets with fewer pixels.