Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis

Abstract

Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.