基于城市天际线的遥感视频生产与交通信息提取

Remote Sensing Video Production and Traffic Information Extraction Based on Urban Skyline

传统交通信息获取方法较难获取大范围、全覆盖的实时动态交通流信息,提出一种利用城市天际线作为观测平台的城市遥感视频生产与交通信息提取方法。首先,在超高层建筑物拍摄对地观测数据;然后,对原始倾斜视频观测数据进行正射校正,与卫星影像融合生成大范围城市遥感视频数据;最后,训练深度学习模型进行车辆分类识别,基于识别结果计算区域车辆数目及密度。在深圳平安大厦观光层开展数据采集并处理分析,结果表明,所提方法可生产低成本、长时间、大范围、高质量的城市遥感视频,基于该遥感视频开展的车辆检测误识别率和漏识别率低,车辆计数准确率高,可对区域交通流量进行有效监控,准确实时地获取城市内部区域交通情况。

Objectives:It is difficult for the traditional traffic information acquisition method to obtain the real-time dynamic traffic flow information of large scope and full coverage.With urbanization,the increasing number of supertall buildings makes the city skyline a favorable platform for earth observation.This paper studies the urban remote sensing video production and traffic information extraction method using the urban skyline as the observation platform.Methods:First,the earth observation data shooting is carried out in the super-tall buildings.Then,the original oblique observation data is corrected by orthography,which is further fused with the satellite image to generate a large range of remote sensing video data of the city.Finally,we train a deep learning model and use it for vehicle classification and identification.The number and density of vehicles in the area are calculated based on the identification results.Results:This paper carries out data collection and processing analysis on the sightseeing floor of Ping,an Building in Shenzhen.Results show that the proposed method can produce low-cost,long-time,large-scale and highquality urban remote sensing video.The vehicle detection based on the remote sensing video have high accuracy.Conclusions:The proposed framework can be used to effectively monitor the regional traffic flow and sever for the smart city management.Furthermore,based on its capability of long-time,high-resolution,full-coverage and real-time remote sensing,this method can also be applied to many other urban management fields,such as urban disaster emergency response,crowd monitoring,and construction site monitoring.