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• W2848487538 abstract "The role of anomaly detection in hyperspectral imaging is increasingly important. Traditional anomaly detection methods mainly extract information from background images. They use this information to find the difference between anomalies and background. Using generally background information for detecting anomalies and modeling background can cause background contamination with anomaly pixels. However, Low — Rank and Sparse Matrix Decomposition (LRaSMD) based methods can solve this problem due to using both background and anomaly information. In this study, an LRaSMD based anomaly detection method is adopted. According to the experimental results, the proposed method shows better performance than other state-of-art methods." @default.
• W2848487538 created "2018-07-19" @default.
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• W2848487538 date "2018-05-01" @default.
• W2848487538 modified "2023-10-17" @default.
• W2848487538 title "Anomaly detection in hyperspectral data with matrix decomposition" @default.
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• W2848487538 doi "https://doi.org/10.1109/siu.2018.8404658" @default.
• W2848487538 hasPublicationYear "2018" @default.
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