用查表法校正高分卫星影像大气影响，提升城市土地利用分类精度。
An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagery
- 基于查表法模拟大气辐射传输，校正卫星影像大气干扰
- 在少量标注数据下仍保持多类地物分割稳定准确
- 适用于印度城市的高分辨率遥感影像分析
将细粒度多光谱影像与深度学习模型结合，已显著推动土地利用与土地覆盖（LULC）分类。然而，传感器在大气顶层测得的数字数值受大气效应影响，需校正为地表反射率以确保分析可靠性。本研究采用基于查表的辐射传输模拟，估算高分辨率CARTOSAT-3多光谱（MX）影像在多个印度城市中的大气路径反射率与透射率，实现大气校正。校正后的地表反射率数据被用于监督与半监督分割模型，在建筑物、道路、树木和水体等多类地物的分割中表现出稳定性，尤其在标注样本稀疏的场景下依然可靠。
原文摘要 · Abstract (English)
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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