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- W3100364246 abstract "Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionary constructed from some online spectral libraries. Based on the proposed method, two strategies are briefly outlined and evaluated to realize the target detection on both synthetic and real experiments." @default.
- W3100364246 created "2020-11-23" @default.
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- W3100364246 date "2019-08-01" @default.
- W3100364246 modified "2023-10-11" @default.
- W3100364246 title "Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery" @default.
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- W3100364246 doi "https://doi.org/10.1109/tgrs.2019.2897635" @default.
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