Joint Kurtosis-Skewness-Based Background Smoothing for Local Hyperspectral Anomaly Detection

摘要

Anomaly detection becomes increasingly important in hyperspectral data exploitation due to the use of high spectral resolution to uncover many unknown substances which cannot be visualized or known a priori. The RX detector is one of the most commonly used anomaly detections algorithms, where both the global and local versions are studied. In the double window model of local RX detection, it is inevitable that there will be abnormal pixels in the outer window where the background information is estimated. These abnormal pixels will cause great interference to the detection result. Aiming at a better estimation of the local background, a joint kurtosis-skewness algorithm is proposed to smooth the background and get better detection results. The skewness and kurtosis are three and four order statistics respectively, which can express the non-Gaussian character of hyperspectral image and highlight the abnormal information of the target. The experimental results show that the proposed detection algorithm is more effective for both synthetic and real hyperspectral images.