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- W4386804564 abstract "This research paper focuses on the important method of image matching for collecting ground control points and automated precise geo-registration of high-resolution satellite imagery. The study aims to improve the matching success rate between incoming satellite images and reference chips generated from aerial color ortho-images by using pan-sharpened satellite images. The results show that the use of pan-sharpened images leads to higher matching success rates due to similar spectral range and higher spatial resolution. The paper also highlights the significance of accurate land cover information for various geospatial applications such as agriculture, environmental and urban management. Traditional methods of gathering land cover information are time-consuming and involve physical labor, making automated methods a more efficient and practical solution." @default.
- W4386804564 created "2023-09-17" @default.
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- W4386804564 date "2023-09-15" @default.
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- W4386804564 title "Deep Learning-Based Land Classification: A CNN Architecture for High-Accuracy Land Use Mapping" @default.
- W4386804564 doi "https://doi.org/10.36948/ijfmr.2023.v05i05.6430" @default.
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