An Approach for Subpixel Anomaly Detection in Hyperspectral Images

Fast detecting difficult targets such as subpixel objects is a fundamental challenge for anomaly detection (AD) in hyperspectral images. In an attempt to solve this problem, this paper presents a novel but simple approach based on selecting a single feature for which the anomaly value is the maximum. The proposed approach applied in the original feature space has been evaluated and compared with relevant state-of-the-art AD methods on Target Detection Blind Test data sets. Preliminary results suggest that the proposed method can achieve better detection performance than its counterparts. The results also show that the proposed method is computationally expedient.