Detecting damaged buildings caused by earthquake using local gradient orientation entropy statistics method

This paper presents a new method to detect damaged buildings caused by earthquake from high spatial resolution remote sensing image. We found that the probability of multiple gradient orientations is greater in a local area within a damaged building than that in a local area within an intact building. Therefore, a new feature (Local Gradient Orientation Entropy, LGOE) was put forward to determine whether a building was damaged. First, gradient information was obtained by Prewitt gradient operator. Second, the gradient orientation entropy of one pixel was calculated in a local 3 ×3 window. Last, average LGOE value within a building boundary was counted. In general, damaged buildings have higher LGOE values because of their irregular texture. Therefore, an optimum LGOE threshold value (LGOE_T) was set to detect damaged buildings. The experiment results of Yushu earthquake using a Quickbird image demonstrated that our method was effective. Of the total 101 buildings, 87 were detected correctly, the overall accuracy was 86.14%, and the overall kappa coefficient is 72.25%.