Detection of collapsed buildings with the aerial images captured from UAV

Abstract

In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle (UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high value. The candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise, each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.

摘要

中文摘要

本文提出了一种依靠低空无人机航拍图像进行坍塌建筑物自动识别的实时灾情评估方法。有别于传统的广域灾情粗略评估系统, 本方法依靠低空无人机实现了对村镇级别小范围区域的坍塌建筑物实时自动识别。本文创新点包括: 1) 结合航拍图像中每个像素点的外形和运动特征, 利用统计方法提取出坍塌建筑物上的有效特征点并抑制背景噪声; 2) 通过在线聚类算法实时提取出疑似坍塌建筑物区域; 3) 通过时空追踪算法对疑似区域进一步筛选, 排除误报结果。