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

作者：Siqi He, Lihong Zhu, Yixuan Li, Qing Xia, Qiong Zheng, Zheng Wang, Xinyu Zou · 发表于：Geocarto International · 年份：2025 · DOI：10.1080/10106049.2025.2521833 · 被引用次数：3 · 研究领域：Urban Heat Island Mitigation、Impact of Light on Environment and Health、Wind and Air Flow Studies

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-2 spectral information, texture features, and vegetation indices, and analyze the contribution of each feature using the SHAP method. The change analysis of the impervious layer in Changsha County from 2016 to 2024 was conducted based on the better machine learning algorithm and indicators for extracting the impervious layer. The outperformed RF algorithm had an overall classification accuracy of over 92% from 2016 to 2024. Interestingly, there was a notable rise of the area of impervious surfaces in 2019 and a substantial fall in 2023, whereas the other years saw slight changes. The suggested approach can serve as a helpful guide for extracting the impervious layer chosen via index optimization.