Hyperspetral Anomaly Detection Incorporating Spatial Information

Abstract

Most anomaly detection methods for hyperspectral image (HSI) have focused on the spectral information while ignoring the spatial information. In this paper, a novel anomaly detection method has been proposed in which the spatial information has been incorporated. Firstly, the dual windows are established to estimate the background of pixel under test (PUT). Secondly, the spectral distance is calculated between PUT and its background to measure its spectral anomaly degree. Then, the principal component analysis is performed on HSI and the spatial anomaly degree of PUT is measured on the first component by comparing the spatial structure similarity between PUT and its background. Lastly, combining the spectral anomaly degree and the spatial anomaly degree, the anomaly degree of PUT is obtained. Experimental results on two hyperspectral datasets confirm the proposed method is superior to three commonly used state-of-the-art anomaly detection methods in suppressing the background and detecting anomalies and is also quite robust to noise.