Hyperspectral Anomaly Detection Using the Spectral-Spatial Graph

Anomaly detection is an important technique for hyperspectral image (HSI) 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 article proposes a new anomaly detection method using the spectral-spatial graph (SSG) that considers both spatial and spectral information. Thus, the spatial adjacency matrix and spectral adjacency matrix are constructed from the spatial and spectral dimensions, respectively. To obtain an SSG with more discriminant characteristics, two different local neighborhood detection strategies are used to measure the similarity of the SSG. Furthermore, global anomaly detection results on HSIs 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 datasets, the proposed algorithm shows superior detection performance.

Automated summary

Anomaly detection is a crucial technique in hyperspectral image processing, aiming to identify pixels significantly different from the background when target spectrum is unavailable. This article introduces a novel anomaly detection method called Spectral-Spatial Graph (SSG) that combines spatial and spectral information, resulting in improved detection performance compared to other algorithms.