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- W3171001481 abstract "Change detection from multitemporal hyperspectral images has attracted great attention. Most traditional methods using spectral information for change detection treat a hyperspectral image as a two-dimensional matrix and do not take into account inherently structure information of spectrum, which leads to limited detection accuracy. To better approximate both spectral and spatial information, a novel three-order Tucker decomposition and reconstruction detector is proposed for hyperspectral change detection. Initially, Tucker decomposition and reconstruction strategies are used to eliminate the influence of various factors in a multitemporal dataset. Specifically, a singular value accumulation strategy is used to determine principal components in factor matrices. Meanwhile, a spectral angle is used to analyze spectral change after tensor processing in different domains. Finally, a new detector is designed to further improve the detection accuracy. Experiments conducted on five real hyperspectral datasets demonstrate that the proposed detector achieves a better detection performance." @default.
- W3171001481 created "2021-06-22" @default.
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- W3171001481 date "2021-01-01" @default.
- W3171001481 modified "2023-10-16" @default.
- W3171001481 title "Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection" @default.
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- W3171001481 doi "https://doi.org/10.1109/jstars.2021.3088438" @default.
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