Hyperspectral Anomaly Detection Using the Spectral–Spatial Graph

Anomaly detection is an important technique for hyperspectral image processing. It aims to find pixels that are markedly different from the background when the target spectrum is unavailable. Many anomaly detection methods have been proposed over the past years, among which graph-based ones have attracted extensive attention. And they usually just consider the spectral information to build the adjacency matrix of the graph, which does not think over the effect of spatial information in this process. This paper proposes a new anomaly detection method using the Spectral-Spatial Graph (SSG) that considers both the spatial and spectral information. Thus, the spatial adjacency matrix and spectral adjacency matrix are constructed from the spatial and spectral dimensions, respectively. To obtain a spectral-spatial graph with more discriminant characteristics, and two different local neighborhood detection strategies are used to measure the similarity of the SSG. Furthermore, global anomaly detection results on hyperspectral images were obtained by the graph Laplacian anomaly detection method and the global and local anomaly detection results were optimized by the differential fusion method. Compared with other anomaly detection algorithms on several synthetic and real data sets, the proposed algorithm shows superior detection performance.