Hyperspectral Anomaly Detection via Optimal Kernel and High-Order Moment Correlation Representation

Hyperspectral anomaly detection has been a hot topic in the field of remote sensing due to its potential application prospects. However, anomaly detection still has two typical problems to be solved. First, the target in the hyperspectral image is usually mixed with the background. Thus, the background is often misjudged as a target without an effective background suppression strategy resulting in false alarms. Second, the non-Gaussian distribution of the complex background affects the judgment of the detector on the abnormality of the target and leads to missed detection. This article proposes an anomaly detection method based on the optimal kernel and high-order moment correlation representation. In selecting the kernel function, the advantage of the Gaussian kernel in anomaly detection is demonstrated through the analysis of the signal uncertainty principle. On this basis, the optimal kernel is determined with sufficient background suppression by isolation forest. In addition, to reduce the influence of non-Gaussian distribution data on detection, the proposed method adopts high-order moments for statistical correlation representation, which further enhances the separability of the background and target after kernel mapping. The experimental results show that the proposed method can effectively suppress the background. Furthermore, it is confirmed that the Gaussian kernel function is effective in improving anomaly detection accuracy. Moreover, the high-order moment correlation representation can highlight the target after kernel mapping, thereby reducing the false alarm rate and obtaining a better detection result.