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• W3181727745 abstract "With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance." @default.
• W3181727745 created "2021-07-19" @default.
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• W3181727745 date "2022-01-01" @default.
• W3181727745 modified "2023-10-18" @default.
• W3181727745 title "Hyperspectral Change Detection Based on Multiple Morphological Profiles" @default.
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• W3181727745 doi "https://doi.org/10.1109/tgrs.2021.3090802" @default.
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