Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes-Reference-Cited by-同舟云学术

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Published:2016-09-23 Issue:10 Volume:8 Page:792
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Wieland Marc,Liu Wen,Yamazaki Fumio

Funder

Japan Society for the Promotion of Science

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

http://www.mdpi.com/2072-4292/8/10/792/pdf

Reference40 articles.

1. Change detection techniques

2. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques

3. Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

4. Earthquake Damage Detection in Urban Areas Using Curvilinear Features

5. An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

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