A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data

A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data

Accurate and timely access to information about impervious layers is essential for urban development and ecological environment. This study employs the random forest (RF) and extreme gradient boosting algorithm to rank the significance of features, which include sentinel-1 polarization and sentinel-...

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Bibliographic Details
Main Authors: Siqi He, Lihong Zhu, Yiman Li, Qing Xia, Qiong Zheng, Zheng Wang, Xinyu Zou
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Geocarto International
Subjects:
Impervious surface
random forest
XGBoost
Sentinel-1/2
feature selection
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2025.2521833
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https://www.tandfonline.com/doi/10.1080/10106049.2025.2521833

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