Yingxiao Xu, Long Pan, C. Du, Jun Li, N. Jing, Jiangjiang Wu
{"title":"基于视觉的无人机航拍图像定位研究进展","authors":"Yingxiao Xu, Long Pan, C. Du, Jun Li, N. Jing, Jiangjiang Wu","doi":"10.1145/3281548.3281556","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.","PeriodicalId":231184,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","volume":"119 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Vision-based UAVs Aerial Image Localization: A Survey\",\"authors\":\"Yingxiao Xu, Long Pan, C. Du, Jun Li, N. Jing, Jiangjiang Wu\",\"doi\":\"10.1145/3281548.3281556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.\",\"PeriodicalId\":231184,\"journal\":{\"name\":\"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery\",\"volume\":\"119 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3281548.3281556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3281548.3281556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vision-based UAVs Aerial Image Localization: A Survey
Unmanned aerial vehicles (UAVs) have been increasingly used in earth observation, public safety, military and civilian applications due to its portability, high mobility and flexibility. In some GPS-denied environments, accurate drone position cannot be obtained due to occlusion, multi-path interference and other factors. While understanding and localization the content of the images is vital for earth observation, map revision, multi-source image fusion, disaster relief, smart city and other applications. The progress of computer vision and convolutional neural networks(CNNs) in image processing provide a promising solution to locate UAVs aerial image and mapping to the large-scale reference image. Firstly, key localization techniques based on image retrieval-----image description, image matching and position mapping are summarized considering the characteristics of UAVs aerial images. And then, image localization based on extracting deep semantic features and image localization based on classification method by subdividing areas are recommended. Throughout this paper, we will have an insight into the prospect of the UAVs image localization and the challenges to be faced.