Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface

The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine lea...

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Bibliographic Details
Main Authors: Zhenfeng Shao, Muhammad Nasar Ahmad, Akib Javed
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Remote Sensing
Subjects:
data fusion
impervious surface
Landsat 8
random forest
XGBoost
Online Access:https://www.mdpi.com/2072-4292/16/4/665

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