Hyperspectral Band Selection for Spectral–Spatial Anomaly Detection

Abstract

Owing to significantly improved spectral resolution, a hyperspectral imaging sensor can now uncover many unknown subtle material substances. In many cases, anomalies are usually embedded in the background. To develop a means through which these anomalies may be detected and separated from the background, we propose a spectral-spatial anomaly detection method based on a selected band subset. To be specific, we constrain an unsupervised network by making full use of the underlying physical characteristics which are beneficial to hyperspectral anomaly detection. Based on that, a selection criterion is constructed to adaptively select a subset of bands that essentially contain discriminative and informative features between the anomaly and background in an unsupervised manner. Then, the selected bands are simultaneously inputted into the spatial detector and spectral detector. To overcome the deficiencies of detecting anomalies in only one aspect, an adaptive combination of spatial result and the spectral result is introduced. Finally, a simple and powerful iterative suppression is conducted on the initial detection map to further reduce false alarm rate while ensuring detection capability. Extensive empirical researches performed on eighteen publicly available hyperspectral images (HSIs) of different sizes over different scenes demonstrate that our proposed method can achieve an average detection capability of 0.99564, and the average false alarm rate is one order of magnitude lower than the second one.