摘要
图像地理定位是将没有地理位置的图像,通过一系列方法获得对应地理位置,使其与现实地理空间建立关联映射的技术。该技术对进一步挖掘图像信息有着重要的作用,在网络空间测绘、情报获取、用户室外定位、增强现实等方面具有较高的应用价值。尽管计算机视觉领域取得了巨大的进展,但是由于图像地理定位涉及到图像特征提取、大规模数据检索、大规模点云处理、深度学习、地理信息特征提取、几何建模与推理、语义场景理解、基于上下文的推理、多数据融合应用等多个领域,所以对图像的高精度自动地理定位仍是需要进一步解决的难题。本文对图像地理定位研究进展进行了梳理,主要包括图像地理定位方法、图像地理定位数据集、图像地理定位评价方法和图像地理定位总结与展望4个方面。首先按照研究内容相关性将图像地理定位方法分为了图像检索、2D-3D匹配和跨模态检索3类方法,并详细介绍了每一类方法的最新研究进展;其次对图像地理定位研究所用的数据集和评价方法进行了归类与总结;最后分析了图像地理定位的研究现状,并从全球地理定位、自然区域地理定位、多方法融合地理定位、基于POI数据的地理定位和预选位置的精细化定位等方面对图像地理定位的未来研究方向进行了展望。
Image geo-localization is a technique that obtains the geographic location information of an image through a series of methods,so as to establish a mapping relationship with the real geographic space.This technique is important for further image information mining and has potential application value in cyberspace surveying and mapping,intelligence acquisition,user outdoor positioning,and augmented reality.Despite the tremendous progress in the field of computer vision,high-precision automatic geo-localization of images still needs to be addressed due to the involvement of multiple fields such as image feature extraction,large-scale data retrieval,large-scale point cloud processing,deep learning,geographic information feature extraction,geometric modeling and reasoning,semantic scene understanding,context-based reasoning,and multiple data fusion.This paper reviews the progress of image geo-localization research,mainly including image geo-localization methods,image geo-localization datasets,image geo-localization evaluation methods,and summary and prospect of image geo-localization.Firstly,image geolocation methods are classified into three categories,i.e.,image retrieval,2D-3D matching,and cross-modal retrieval,according to the relevance of the research content.Secondly,the datasets and evaluation methods used for image geo-localization research are categorized and summarized.The geolocalization datasets include image datasets,cross-view datasets,Structure from Motion(SfM)datasets,and multimodal datasets,etc.The image geo-localization evaluation metrics include Top-k candidates,localization error,position and orientation error per video frame,and accuracy/recall.Finally,the current status of image geo-localization research is analyzed,and the future research directions of image geo-localization are outlined in terms of global geo-localization,natural area geo-localization,multi-method fusion for geo-localization,Point of Interest(POI)data-based geo-localization,and pre-selected location refinement.
作者
黄高爽
周杨
胡校飞
赵璐颖
张呈龙
HUANG Gaoshuang;ZHOU Yang;HU Xiaofei;ZHAO Luying;ZHANG Chenglong(Institute of Geospatial Information,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2023年第7期1336-1362,共27页
Journal of Geo-information Science
基金
河南省自然科学基金项目(202300410536)。
关键词
图像地理定位
网络空间测绘
图像检索
多源数据
评价方法
数据集
image geo-localization
cyberspace surveying and mapping
image retrieval
multi-sources data
evaluation method
datasets
