高分辨率遥感图像灾区建筑物检测

摘要

Building detection in disaster area is pivotal in collecting disaster information and implementing post disaster rescue.Aiming at detecting buildings in disaster area from remote sensing images wtih high-resolution,an improved multi-directional and multi-scale segmentation algorithm based on morphological features is proposed to implement automated detection of buildings in disaster area.Firstly,we integrate the properties of morphological operators (e.g.,reconstruction,granularity,and directionality) into the implicit characteristics of buildings (e.g.,brightness,size,and contrast) to extract bright and high-contrast buildings.Then,the regional image edge information is combined to extract potential buildings.Experimental results show that the proposed method has a higher detection rate and a low false rate in detecting buildings of disaster area.%在遥感图像中,灾区建筑物的检测对灾情获取和灾后应急救援具有重要意义.针对灾区高分辨率遥感图像中建筑物检测的问题,提出了一种改进的基于形态学特征的多方向和多尺度分割方法,以实现灾区建筑物的自动化检测.首先将形态学算子的重建、粒度和方向等性质整合到建筑物的亮度、大小和对比度等特征中,对遥感图像进行初步的分割并提取高亮和高对比度的建筑物,然后结合图像的区域边缘信息,进一步提取潜在的建筑物.实验结果表明,所提方法对灾区高分辨率图像中的建筑目标有较高的检测率和较低的误检率.