Hyperspectral Anomaly Detection for Spectral Anomaly Targets via Spatial and Spectral Constraints

Anomaly detection in a hyperspectral image (HSI) has been actively researched in the field of remote sensing due to its significant application requirements. Traditional methods were based on the spatial models for the background to detect the anomaly targets. However, in detecting the spectral anomaly targets, they led to two problems: 1) the spatial characteristics of spectral anomaly targets are not obvious, which causes many false alarms in detection and 2) spectral anomaly usually occurs in the local band of targets, while the rest of the spectrum is similar to the ones of surrounding backgrounds, which leads to missed detection. This article proposes a novel hyperspectral anomaly detection method for spectral anomaly targets based on spatial and spectral constraints (SASCs). This model finds suspected anomaly target part as spatial anomaly results through SASCs. Then, the feedback process determines the spectral anomaly through the spectral difference between the tested pixel and the surrounding background. It is fed back to the spatial anomaly results to obtain final detection results. Furthermore, in order to enlarge the spectral difference between anomaly and background while suppressing the background, the optimal order of fractional Fourier transform (FrFT) is determined by combining spatial anomaly results with the uncertainty principle, which is used in FrFT of HSI. Experimental results show that the proposed method suppresses the background and reduces the false alarm rate. The feedback mechanism effectively reduces the missing detection rate, achieving a promising detection accuracy.