Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random...

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Bibliographic Details
Main Authors: Sandra Maria Cherian, Rajitha K
Format: Article
Language:English
Published: IWA Publishing 2024-01-01
Series:Journal of Water and Climate Change
Subjects:
google earth engine (gee)
jeffries–matusita distance
mangrove
random forest classification
sentinel-1
synthetic aperture radar (sar)
Online Access:http://jwcc.iwaponline.com/content/15/1/29

Internet

http://jwcc.iwaponline.com/content/15/1/29

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