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- W4310346658 abstract "Quickly and accurately obtaining the area, volume and distribution of surface water is of great significance to the resource survey, urban planning, economic development, flood disaster monitoring, etc. In this paper, the domestic Gaofen-3 (GF-3) dual-polarization radar satellite data is used, combined with the semantic segmentation algorithm of the deep learning attention mechanism, to construct a polarized radar surface water information extraction model based on TransUnet, which has been carried out in the Poyang Lake area. The results show that the model can not only give full play to the advantages of radar water body information extraction, but also better ensure the accuracy of water body edges and reduces the interference of shadows and radar image speckle noise caused by terrain, overlay, foreshortening, etc. The experimental result achieves an overall classification accuracy of 95.8% and a Kappa coefficient value of 0.942, which are both better than traditional threshold segmentation algorithms and support vector machine classification algorithms." @default.
- W4310346658 created "2022-12-09" @default.
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- W4310346658 date "2022-11-30" @default.
- W4310346658 modified "2023-10-15" @default.
- W4310346658 title "Application Research on Water Body Extraction of Gaofen-3 Polarimetric SAR Based on Deep Learning" @default.
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- W4310346658 doi "https://doi.org/10.1007/978-981-19-8202-6_24" @default.
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