Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture...

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Bibliographic Details
Main Authors: Yuewan Wu, Jiayi Pan
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Remote Sensing
Subjects:
impervious surface
Landsat-8
Sentinel-1
CBERS-04
random forest
fusion
Online Access:https://www.mdpi.com/2072-4292/15/22/5387

Internet

https://www.mdpi.com/2072-4292/15/22/5387

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