Аңдатпа
Comparison of other satellite data, there are fewer scientific papers about building damage assessment using Sentinel-1 data. Because many scientists ignore it due to middle-spatial resolution, the general trend is using high-resolution data (TerraSAR-X, COSMO-SkyMed, etc.) for that purpose. It is related to the problem that middle-resolution data has lower overall accuracy than high resolution. Sentinel-1 data is more freely available than others. Pre-event data is always available. The application of texture-based change detection techniques can be used to improve overall accuracy. Better separation of completely destroyed and intact buildings was achieved using homogeneity and dissimilarity textural parameters computed from the grey-level co-occurrence matrices (GLCM). The backscattering coefficients with dual polarization (VV, VH) and the coherence coefficient (pre-earthquake and coseismic data) were exploited for this study. The best relevant GLCM textural parameter variables were determined to extract open areas (without buildings), and damaged and untouched buildings in urban areas using supervised classification methods. In this research work, the overall accuracy was achieved at 0.77. The producer's accuracy for open areas is 0.84, for the case of a damaged building 0.85, and for untouched building 0.64. Beijing-2 high-resolution optical data and Copernicus Emergency Management Service data were exploited for that classification. Amatrice town as a study area chose for investigation as an example that was significantly affected by the earthquake in Central Italy in 2016.