An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery

• URL: http://arxiv.org/abs/2409.05494v2
• Date: Tue, 10 Sep 2024 06:15:55 GMT
• Title: An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery
• Authors: Soham Mukherjee, Yash Dixit, Naman Srivastava, Joel D Joy, Rohan Olikara, Koesha Sinha, Swarup E, Rakshit Ramesh,
• Abstract summary: This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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