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

arXiv: https://arxiv.org/abs/2409.05494 · HTML: https://mlsanity.com/paper/2409.05494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Images](https://mlsanity.com/paper/2408.02382/) (2024)

## Built on by (most cited first, 1 of 1)

- [Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover](https://mlsanity.com/paper/2507.18099/) (2025)

## Abstract (arXiv)

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.