Preprint Article Version 1 This version is not peer-reviewed

Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters

Version 1 : Received: 18 January 2020 / Approved: 20 January 2020 / Online: 20 January 2020 (10:19:38 CET)

How to cite: Akhmadiya, A.; Nabiyev, N.; Moldamurat, K.; Dyusekeev, K.; Atanov, S. Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters. Preprints 2020, 2020010225 (doi: 10.20944/preprints202001.0225.v1). Akhmadiya, A.; Nabiyev, N.; Moldamurat, K.; Dyusekeev, K.; Atanov, S. Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters. Preprints 2020, 2020010225 (doi: 10.20944/preprints202001.0225.v1).

Abstract

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

Subject Areas

radar remote sensing; building damage assessment; change detection method; GLCM

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