From Difference to Similarity: A Manifold Ranking-Based Hyperspectral Anomaly Detection Framework

Most of the existing hyperspectral anomaly detectors only consider the difference between anomaly pixels and background pixels. These methods may mistakenly detect some regions in a complex background that contains various ground covers, since some background regions and anomaly objects may have similar spectral or spatial characteristics. Therefore, with a new perspective, this paper introduces a novel manifold ranking-based detection framework (MRDF). In addition to capturing the difference between anomaly pixels and background pixels, this detection framework exploits the similarity between anomaly pixels for detection. Specifically, the proposed detection framework comprises three main steps. First, the Reed-Xiaoli method is applied to capture the spectral difference between anomaly pixels and background pixels, and an initial detection map can be obtained. A set of anomaly queries are obtained automatically by employing the binary segmentation to the initial detection map. Then, we construct a closed-loop graph to characterize the spatial similarity between adjoining nodes where each node is a superpixel. Finally, a manifold ranking technique is employed to estimate the ranking value of every node based on the similarity between the test node and anomaly queries. By normalizing the ranking value of each node, a final detection map is generated. Abundant experiments are conducted on four real hyperspectral data sets. It is found that the proposed detection framework obtains a better detection performance than the current state-of-the-art detectors.