SAR COHERENCE change detection of urban areas affected by disasters using Sentinel-1 imagery

16Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

The study focuses on two study areas: san Juan in Puerto Rico, which was affected by Hurricane Maria in September 2017, and Sarpol Zahab in Iran, which was one of the towns affected by an earthquake in November 2017. In our study, we generate coherence images, and classify them into areas of 'change' and 'no-change'. A statistical analysis is made by converting the coherence results into point data, creating street blocks for the study areas and integrating the point data into the street blocks to calculate the standard deviation over the whole stack of images. Additionally, Landsat imagery is used to create land-use classes, convert them to polygons and integrate the polygon classes to the coherence maps to determine the average coherence loss per class for each disaster. Results show 65% loss in coherence after the earthquake in Sarpol-e-Zahab and 75% loss in Puerto Rico after the Hurricane. Land-use classes show coherence losses to below 0.5 for each disaster.

Cite

CITATION STYLE

APA

Washaya, P., & Balz, T. (2018). SAR COHERENCE change detection of urban areas affected by disasters using Sentinel-1 imagery. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 1857–1861). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-3-1857-2018

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free