Use of Sentinel-1 Dual Polarization Multi-Temporal Data with Gray Level Co-Occurrence Matrix Textural Parameters for Building Damage Assessment

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. The main task of this research paper is the collection of most relevant scientific research in the field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in the present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in the 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 recommended as free available for monitoring of destruction on a degree microregion level. Change detection and texture-based methods are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found better for the separation of collapsed and intact buildings. Dual polarization (VV, VH) backscattering coefficients and coherence coefficients (before the 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 the 2016 Central Italy Earthquake.