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- W3131736134 abstract "Researchers have already verified that the deep learning (DL) technology can realize accurate and robust mapping of tropical cyclone-induced coastal inundation in synthetic aperture radar imagery. In order to liberate the DL-based inundation mapping from human supervision, we propose to use the clustering of deep convolutional autoencoder-generated features. The mapping results of Lekima 2019-induced inundation demonstrate the advantages and availability of the proposed method." @default.
- W3131736134 created "2021-03-01" @default.
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- W3131736134 date "2020-09-26" @default.
- W3131736134 modified "2023-09-24" @default.
- W3131736134 title "Automatic Mapping of Tropical Cyclone-Induced Coastal Inundation in SAR Imagery Based on Clustering of Deep Features" @default.
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- W3131736134 doi "https://doi.org/10.1109/igarss39084.2020.9324529" @default.
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