{"title":"基于目标分割和变化分析的卫星图像路线图更新","authors":"Xia Wei, Sun Shikai, L. Jian","doi":"10.1109/PRRS.2018.8486330","DOIUrl":null,"url":null,"abstract":"This paper studies to detect the change of road network from remote sensing images. Our purpose is to apply the method for practical usages, such as navigation map updating, road construction supervision, disaster survey, and so on. The proposed approach assumes that there is an outdated road map and the updating job is performed by detecting new road network and comparing the changes. The deep convolution network is utilized for precisely segmenting road areas. An image registration and correction procedure is performed to unify the spatial coordinate reference between the old map and the new road detection results. Then, we modify and standardize the extracted road segments, and apply it to determine the road variation of different periods. Experiments show that, the proposed method successfully identifies road changes, which is useful for fast map update in remote areas.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Road Map Update from Satellite Images by Object Segmentation and Change Analysis\",\"authors\":\"Xia Wei, Sun Shikai, L. Jian\",\"doi\":\"10.1109/PRRS.2018.8486330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies to detect the change of road network from remote sensing images. Our purpose is to apply the method for practical usages, such as navigation map updating, road construction supervision, disaster survey, and so on. The proposed approach assumes that there is an outdated road map and the updating job is performed by detecting new road network and comparing the changes. The deep convolution network is utilized for precisely segmenting road areas. An image registration and correction procedure is performed to unify the spatial coordinate reference between the old map and the new road detection results. Then, we modify and standardize the extracted road segments, and apply it to determine the road variation of different periods. Experiments show that, the proposed method successfully identifies road changes, which is useful for fast map update in remote areas.\",\"PeriodicalId\":197319,\"journal\":{\"name\":\"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRRS.2018.8486330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRRS.2018.8486330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Road Map Update from Satellite Images by Object Segmentation and Change Analysis
This paper studies to detect the change of road network from remote sensing images. Our purpose is to apply the method for practical usages, such as navigation map updating, road construction supervision, disaster survey, and so on. The proposed approach assumes that there is an outdated road map and the updating job is performed by detecting new road network and comparing the changes. The deep convolution network is utilized for precisely segmenting road areas. An image registration and correction procedure is performed to unify the spatial coordinate reference between the old map and the new road detection results. Then, we modify and standardize the extracted road segments, and apply it to determine the road variation of different periods. Experiments show that, the proposed method successfully identifies road changes, which is useful for fast map update in remote areas.