Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection






Citation of Original Publication

C. -I. Chang, "Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection," in IEEE Transactions on Geoscience and Remote Sensing, 2022, doi: 10.1109/TGRS.2022.3211696.


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— A known target detection assumes that the target to be detected is provided a priori, while anomaly detection is an unknown target detection without any prior knowledge. As a result, known target detection generally performs searchbefore-detect detection in an active mode, referred to as active target detection as opposed to anomaly detection, which performs throw-before-detect detection in a passive mode, referred to as passive target detection. Accordingly, techniques designed for these two types of detection are completely different. This article shows that there is indeed a mechanism, called target-to-anomaly conversion, which can convert hyperspectral target detection (HTD) to hyperspectral anomaly detection (HAD) via a novel idea, called dummy variable trick (DVT). By virtue of such target-to-anomaly conversion many well-known target detection techniques, such as likelihood ratio test (LRT), constrained energy minimization (CEM), and orthogonal subspace projection (OSP), the spectral angle mapper (SAM) and the adaptive cosine estimator (ACE) can be converted to their corresponding anomaly detectors, referred to as target-to-anomaly conversion-derived anomaly detectors (TAC-ADs). Since a target detector requires target knowledge while TAC-AD does not, a direct use of TACAD is not effective. To make TAC-AD work, a newly developed approach to effective anomaly space (EAS) is implemented in conjunction with TAC-AD so that anomalies can be retained in EAS and interference, and noise including background (BKG) can be removed from EAS. The experiments demonstrate that TAC-AD operating in EAS performs better than many existing anomaly detection approaches, including model-based methods.