{"title":"基于Faster RCNN的遥感影像建筑物高度恢复方法","authors":"Biao Li, Xucan Chen, Zuo Lin","doi":"10.1109/ICTAI56018.2022.00146","DOIUrl":null,"url":null,"abstract":"To accurately obtain building height information from a single remote sensing image, we propose a height restoration method, which mainly is composed of two parts, building shadow rotation detection and building height calculation. The first part adds a skip connection structure and rotated branches based on Faster RCNN and achieves rotation shadow detection. The latter uses imaging date and geographic latitude to restore building height based on the geometric relationship between the building and its shadow. The experiment shows that the accuracy of height restoration is 95.04%. Compared with the state-of-the-art method, our method has the superiority of simple implementation, less data, fast speed, and high accuracy.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Building Height Restoration Method of Remote Sensing Images based on Faster RCNN\",\"authors\":\"Biao Li, Xucan Chen, Zuo Lin\",\"doi\":\"10.1109/ICTAI56018.2022.00146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To accurately obtain building height information from a single remote sensing image, we propose a height restoration method, which mainly is composed of two parts, building shadow rotation detection and building height calculation. The first part adds a skip connection structure and rotated branches based on Faster RCNN and achieves rotation shadow detection. The latter uses imaging date and geographic latitude to restore building height based on the geometric relationship between the building and its shadow. The experiment shows that the accuracy of height restoration is 95.04%. Compared with the state-of-the-art method, our method has the superiority of simple implementation, less data, fast speed, and high accuracy.\",\"PeriodicalId\":354314,\"journal\":{\"name\":\"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI56018.2022.00146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI56018.2022.00146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Building Height Restoration Method of Remote Sensing Images based on Faster RCNN
To accurately obtain building height information from a single remote sensing image, we propose a height restoration method, which mainly is composed of two parts, building shadow rotation detection and building height calculation. The first part adds a skip connection structure and rotated branches based on Faster RCNN and achieves rotation shadow detection. The latter uses imaging date and geographic latitude to restore building height based on the geometric relationship between the building and its shadow. The experiment shows that the accuracy of height restoration is 95.04%. Compared with the state-of-the-art method, our method has the superiority of simple implementation, less data, fast speed, and high accuracy.