Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood-Reference-Cited by-同舟云学术

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

Published:2015-03-23 Issue:3 Volume:7 Page:3372-3399
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Ireland Gareth,Volpi Michele,Petropoulos George

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

http://www.mdpi.com/2072-4292/7/3/3372/pdf

Reference82 articles.

1. Increased Frequency of Low-Magnitude Floods in New England1

2. Groundwater change forecasts widespread forest dieback across an extensive floodplain system

3. Evidence for flash floods over deserts from loss of coherence in InSAR imagery

4. Flood risk and flood management

5. Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review

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