The synthetic aperture radar SAR system with the capability of imaging during the night, day, and the all-weather conditions has a high potential in change detection on the ground surface. In this research, we used three SAR images of ALOS-2 satellite over Sarpole-Zahab town in the west of Iran that had an earthquake with the magnitude of 7.3 on November 12, 2017. The effects of speckle noise on the accuracy of the results were assessed based on noise reduction filters. Correlation coefficient, difference of intensity (in five window sizes), and difference of coherence and texture (in six window sizes) of the pre- and post-event images were calculated, and the output parameters were extracted. Then, the damage assessment was carried out based on four machine learning classifiers, containing the random forest (RDF), the support vector machine, the naive Bayes classifier, and K-nearest neighbor. The RDF showed an overall accuracy of 86.3%. Seventy percent of the dataset was used for training, and 30% of it was used for the prediction purpose (~ 300 buildings). Based on the training dataset, the total number of structures in the study area was predicted (approximately 9200 buildings). Finally, a discriminant analysis was carried out among the damaged and undamaged buildings.

具有夜，白天和全天候成像能力的合成孔径雷达SAR系统在探测地面变化方面具有很大的潜力。在这项研究中，我们使用了伊朗西部萨尔波勒-扎哈布镇上空的ALOS-2卫星的三个SAR图像，该地震在2017年11月12日发生了7.3级地震。散斑噪声对结果准确性的影响根据降噪滤波器进行评估。计算事件前后图像的相关系数，强度差异（在五个窗口大小中）以及相干性和纹理差异（在六个窗口大小中），并提取输出参数。然后，基于四个机器学习分类器（包括随机森林（RDF），支持向量机，朴素的贝叶斯分类器和K近邻。RDF显示总体精度为86.3％。数据集的70％用于训练，其中30％用于预测（〜300座建筑物）。根据训练数据集，可以预测研究区域内的建筑物总数（大约9200栋建筑物）。最后，对受损和未损坏的建筑物进行了判别分析。