GSEAD: Graphical Scoring Estimation for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection has been the subject of increased attention in the past 20 years. One obvious trend for scholars is seeking an appropriate data description in the hyperspectral anomaly detection domain. However, a specific predetermined data model in a given detector may not be able to fit all the other cases of hyperspectral images. Hence, can we construct a hyperspectral anomaly detector from a data-adaptive analysis perspective that can implement detection processing only with the characteristics of the data itself? In our manuscript, we propose a graphical scoring estimation based anomaly detector (GSEAD) that utilizes graphical data description to achieve a data-adaptive analysis-based anomaly detection procedure. First, potential anomalies are screened out by a predicted connected component graph (pcc-graph). The remaining pixels constitute the robust background dataset. Second, an embedded locality preserving graph (elp-graph) is generated with the robust background dataset in an intrinsic manifold space by locality preserving graph embedding. Finally, a k-nearest neighbor graphical scoring estimation is undertaken to output the detection result. A target-embedded hyperspectral dataset and three real hyperspectral images were utilized to validate the detection performance of the proposed method. The experimental results show that GSEAD achieves superior receiver operating characteristic curves, area under ROC curves values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods. A sensitivity analysis of the relevant parameters was also undertaken in the experimental analysis.