Background Annihilated Target-Constrained Interference-Minimized Filter (TCIMF) for Hyperspectral Target Detection

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Background_Annihilated_Target-Constrained_Interference-Minimized_Filter_TCIMF_for_Hyperspectral_Target_Detection.pdf (4.01 MB)

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https://ieeexplore.ieee.org/abstract/document/9896885

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https://doi.org/10.1109/TGRS.2022.3208519
 http://hdl.handle.net/11603/26193

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UMBC Computer Science and Electrical Engineering Department
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https://orcid.org/0000-0002-7838-9293
 https://orcid.org/0000-0002-5450-4891

Date

2022-09-21

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journal articles
postprints
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Citation of Original Publication

J. Chen and C. -I. Chang, "Background Annihilated Target-Constrained Interference-Minimized Filter (TCIMF) for Hyperspectral Target Detection," in IEEE Transactions on Geoscience and Remote Sensing, 2022, doi: 10.1109/TGRS.2022.3208519.

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Abstract

Target-constrained interference-minimized filter (TCIMF) has been widely used in various target detection applications for hyperspectral data exploitation. However, like other classic target detection algorithms, the complex background (BKG) of a scene significantly impacts its performance. To better cope with BKG this paper develops a BKG-annihilated TCIMF (BA-TCIMF) which can be implemented in two stages with BKG annihilation in the first stage followed by target detectability enhancement and target BKG suppression performed by TCIMF in the second stage. In particular, the second stage extracts additional BKG signatures from the BKG-annihilated data as unwanted signatures to enhance target detectability via orthogonal subspace projection (OSP), while suppressing target BKG in the BKG-annihilated data by constrained energy minimization (CEM). Depending upon how these two stages are carried out, three versions of BATCIMF, data sphered BA-TCIMF (DS-BA-TCIMF), low rank and sparse matrix decomposition (LRaSMD) BA-TCIMF (LRaSMD-BA-TCIMF) and component decomposition analysisBA-TCIMF (CDA-BA-TCIMF) are derived. Experimental results demonstrate that BA-TCIMF performs as it is designed and better than many existing target detection algorithms