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- W2989543813 abstract "This paper proposes a new unsupervised band selection method for hyperspectral imagery anomaly detection. Main background subset is identified using global RX detector. An effective criterion of band subset selection is designed based on minimum background variations using differential data of the main background subset. The particle swarm optimization algorithm is used as the search strategy to find the best solution for band selection with the proposed criteria. The proposed method is evaluated by experiments of hyperspectral anomaly detection, and the results show that the performance of anomaly detection is significantly improved after band selection." @default.
- W2989543813 created "2019-11-22" @default.
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- W2989543813 date "2019-07-01" @default.
- W2989543813 modified "2023-09-25" @default.
- W2989543813 title "Anomaly Detection-Oriented Band Selection for Hyperspectral Image" @default.
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- W2989543813 doi "https://doi.org/10.1109/igarss.2019.8898990" @default.
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