摘要
传统的建筑物遥感提取主要是基于人工设计特征在滑窗内提取建筑物信息,具有特征鲁棒性差、检测率不稳定等缺点。本文通过分析航空倾斜摄影影像中建筑物的特点,提出倾斜摄影影像中的建筑物提取必须将建筑物屋顶与建筑物墙体分别提取的观点,在此基础上,引入计算机视觉领域主流的Faster R-CNN目标检测模型,采用改进的Faster RCNN分别对屋顶与墙体进行检测。本文以武汉市航空倾斜摄影影像作为数据集开展实验,将图像中单体建筑作为一类的平均精度均值为89.8%,将建筑物屋顶与墙体分开检测的mAP值为93.5%,表明该方法可有效提高航空倾斜摄影影像中建筑物提取的精度,下一步研究方向为降低墙体的漏检率。
Traditional building remote sensing extraction is mainly based on artificial design features to extract building information in sliding Windows,which has disadvantages such as poor feature robustness and unstable detection rate.Based on the analysis of the characteristics of buildings in aerial oblique photography images,this paper proposes that buildings must be extracted from roof and wall separately.On this basis,the mainstream Faster R-CNN target detection model in the field of computer vision is introduced,and the improved Faster RCNN is used to detect roof and wall respectively.In this paper,Wuhan aerial oblique photography image is used as the data set to carry out the experiment.The average accuracy of taking the single building in the image as a class is 89.8%,and the mAP value of detecting the building roof and wall separately is 93.5%.It shows that this method can effectively improve the accuracy of building extraction in aerial oblique photography image.The next research direction is to reduce the missed detection rate of wall.
作者
焦云清
JIAO Yunqing(School of Public Management,Huazhong Agricultural University,Wuhan,Hubei Province,430070 China)
出处
《科技创新导报》
2021年第26期10-12,共3页
Science and Technology Innovation Herald
关键词
航空倾斜摄影
建筑物提取
深度学习
目标检测
Aerial oblique photography
Building extraction
Deep learning
Target detection
