International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Deep Learning-Based Land Classification: A CNN Architecture for High-Accuracy Land Use Mapping

Author(s) Vinay Tiparadi, Vidya Shinde, Pranav Walgude, Rushikesh Muley, Sohan Sonpatki
Country India
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.
Keywords Image matching, Ground control points, Geo-registration, Satellite imagery.
Field Engineering
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-09-15
Cite This Deep Learning-Based Land Classification: A CNN Architecture for High-Accuracy Land Use Mapping - Vinay Tiparadi, Vidya Shinde, Pranav Walgude, Rushikesh Muley, Sohan Sonpatki - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.6430
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.6430
Short DOI https://doi.org/gsqr35
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