An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Mukherjee, Soham Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan Sinha, Koesha E, Swarup Ramesh, Rakshit |
| author_facet | Mukherjee, Soham Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan Sinha, Koesha E, Swarup Ramesh, Rakshit |
| contents | The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to retrieve accurate Bottom-of-Atmosphere surface reflectance for reliable analysis. This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance for atmospherically correcting high-resolution CARTOSAT-3 Multispectral (MX) imagery for several Indian cities. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models, demonstrating stability in multi-class (buildings, roads, trees and water bodies) LULC segmentation accuracy, particularly in scenarios with sparsely labelled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05494 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery Mukherjee, Soham Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan Sinha, Koesha E, Swarup Ramesh, Rakshit Computer Vision and Pattern Recognition The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to retrieve accurate Bottom-of-Atmosphere surface reflectance for reliable analysis. This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance for atmospherically correcting high-resolution CARTOSAT-3 Multispectral (MX) imagery for several Indian cities. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models, demonstrating stability in multi-class (buildings, roads, trees and water bodies) LULC segmentation accuracy, particularly in scenarios with sparsely labelled data. |
| title | An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.05494 |