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 3x3 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 (LGOET) 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%.