Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection
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Author/Creator ORCID
Date
2022-10-03
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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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Abstract
— 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.