2021
DOI: 10.1109/tgrs.2020.3002724
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Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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Cited by 52 publications
(38 citation statements)
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“…DS can effectively reduce BKG interference, while retaining anomalies after the LRaSMD decomposition and prior to OSP-AD. Despite that there are works reported in [24,49] using OSP to suppress BKG, their used anomaly detectors were still RXD-type anomaly detectors. It seems that using OSP-TD as anomaly detector has not been investigated for AD.…”
Section: Novelties Of Osp-admentioning
confidence: 99%
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“…DS can effectively reduce BKG interference, while retaining anomalies after the LRaSMD decomposition and prior to OSP-AD. Despite that there are works reported in [24,49] using OSP to suppress BKG, their used anomaly detectors were still RXD-type anomaly detectors. It seems that using OSP-TD as anomaly detector has not been investigated for AD.…”
Section: Novelties Of Osp-admentioning
confidence: 99%
“…Most recently, [49] re-derived GoDec as an OSP-GoDec which made use of OSP to re-implement GoDec. In particular, it was the first work ever reported to resolve the issues of determining the parameter, r, rank of Low-rank matrix and k, cardinality of sparsity, both of which have been determined empirically and manually in the past.…”
Section: Related Workmentioning
confidence: 99%
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