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• W4294308923 endingPage "1815" @default.
• W4294308923 startingPage "1815" @default.
• W4294308923 abstract "Hyperspectral imagers are developing towards high resolution, high detection sensitivity, broad spectra, and wide coverage, which means that hyperspectral data are getting more and more substantial. This brings a great challenge to data storage and real-time transmission of hyperspectral data. A compression method based on Tucker decomposition and CANDECOMP/PARAFAC decomposition (TD-CP) is proposed. The hyperspectral data are treated as a third-order tensor. First, TD is performed on the hyperspectral data to obtain a core tensor and three factor matrices, and then CP decomposition is performed on the core tensor. Compared with principal component analysis (PCA)+JPEG2000, TD, and CP, TD-CP can retain spatial information and spectral information better at the same time, and running time is shorter." @default.
• W4294308923 created "2022-09-02" @default.
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• W4294308923 date "2022-09-14" @default.
• W4294308923 modified "2023-10-16" @default.
• W4294308923 title "Compression of hyperspectral images based on Tucker decomposition and CP decomposition" @default.
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• W4294308923 doi "https://doi.org/10.1364/josaa.468167" @default.
• W4294308923 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36215553" @default.
• W4294308923 hasPublicationYear "2022" @default.
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