{"title":"地形复杂性和基于最大泊松盘采样的数字高程模型简化","authors":"Jingxian Dong, Fan Ming, Twaha Kabika, Jiayao Jiang, Siyuan Zhang, Aliaksandr Chervan, Zhukouskaya Natallia, Wenguang Hou","doi":"10.14358/pers.23-00023r2","DOIUrl":null,"url":null,"abstract":"With the rapid development of lidar, the accuracy and density of the Digital Elevation Model (DEM) point clouds have been continuously improved. However, in some applications, dense point cloud has no practical meaning. How to effectively sample from the dense points and maximize the preservation of terrain features is extremely important. This paper will propose a DEM sampling algorithm that utilizes terrain complexity and maximal Poisson-disk sampling to extract key feature points for adaptive DEM sampling. The algorithm estimates terrain complexity based on local terrain variation and prioritizes points with high complexity for sampling. The sampling radius is inversely proportional to terrain complexity, while ensuring that points within the radius of accepted samples are not considered new samples. This way makes more points of concern in the rugged regions. The results show that the proposed algorithm has higher global accuracy than the classic six sampling methods.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":"45 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Terrain Complexity and Maximal Poisson-Disk Sampling-Based Digital Elevation Model Simplification\",\"authors\":\"Jingxian Dong, Fan Ming, Twaha Kabika, Jiayao Jiang, Siyuan Zhang, Aliaksandr Chervan, Zhukouskaya Natallia, Wenguang Hou\",\"doi\":\"10.14358/pers.23-00023r2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of lidar, the accuracy and density of the Digital Elevation Model (DEM) point clouds have been continuously improved. However, in some applications, dense point cloud has no practical meaning. How to effectively sample from the dense points and maximize the preservation of terrain features is extremely important. This paper will propose a DEM sampling algorithm that utilizes terrain complexity and maximal Poisson-disk sampling to extract key feature points for adaptive DEM sampling. The algorithm estimates terrain complexity based on local terrain variation and prioritizes points with high complexity for sampling. The sampling radius is inversely proportional to terrain complexity, while ensuring that points within the radius of accepted samples are not considered new samples. This way makes more points of concern in the rugged regions. The results show that the proposed algorithm has higher global accuracy than the classic six sampling methods.\",\"PeriodicalId\":211256,\"journal\":{\"name\":\"Photogrammetric Engineering & Remote Sensing\",\"volume\":\"45 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering & Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.23-00023r2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering & Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.23-00023r2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
随着激光雷达的快速发展,数字高程模型(DEM)点云的精度和密度不断提高。然而,在某些应用中,密集的点云并没有实际意义。如何有效地对密集点进行采样,并最大限度地保留地形特征就显得极为重要。本文将提出一种 DEM 采样算法,利用地形复杂性和最大泊松盘采样提取关键特征点,进行自适应 DEM 采样。该算法根据局部地形变化估算地形复杂度,并优先对复杂度高的点进行采样。采样半径与地形复杂度成反比,同时确保在接受采样半径内的点不被视为新样本。这样,在崎岖地区就会有更多的点受到关注。 结果表明,与经典的六种采样方法相比,所提出的算法具有更高的全局精度。
Terrain Complexity and Maximal Poisson-Disk Sampling-Based Digital Elevation Model Simplification
With the rapid development of lidar, the accuracy and density of the Digital Elevation Model (DEM) point clouds have been continuously improved. However, in some applications, dense point cloud has no practical meaning. How to effectively sample from the dense points and maximize the preservation of terrain features is extremely important. This paper will propose a DEM sampling algorithm that utilizes terrain complexity and maximal Poisson-disk sampling to extract key feature points for adaptive DEM sampling. The algorithm estimates terrain complexity based on local terrain variation and prioritizes points with high complexity for sampling. The sampling radius is inversely proportional to terrain complexity, while ensuring that points within the radius of accepted samples are not considered new samples. This way makes more points of concern in the rugged regions. The results show that the proposed algorithm has higher global accuracy than the classic six sampling methods.