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DEM generation from contours and a low-resolution DEM

机译:从轮廓和低分辨率DEM生成DEM

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A digital elevation model (DEM) is a virtual representation of topography, where the terrain is established by the three-dimensional co-ordinates. In the framework of sparse representation, this paper investigates DEM generation from contours. Since contours are usually sparsely distributed and closely related in space, sparse spatial regularization (SSR) is enforced on them. In order to make up for the lack of spatial information, another lower spatial resolution DEM from the same geographical area is introduced. In this way, the sparse representation implements the spatial constraints in the contours and extracts the complementary information from the auxiliary DEM. Furthermore, the proposed method integrates the advantage of the unbiased estimation of kriging. For brevity, the proposed method is called the kriging and sparse spatial regularization (KSSR) method. The performance of the proposed KSSR method is demonstrated by experiments in Shuttle Radar Topography Mission (SRTM) 30 m DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m global digital elevation model (GDEM) generation from the corresponding contours and a 90 m DEM. The experiments confirm that the proposed KSSR method outperforms the traditional kriging and SSR methods, and it can be successfully used for DEM generation from contours. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译:数字高程模型(DEM)是地形的虚拟表示,其地形是通过三维坐标建立的。在稀疏表示的框架中,本文研究了从轮廓生成DEM的过程。由于轮廓通常在空间上稀疏分布且紧密相关,因此对它们执行稀疏空间正则化(SSR)。为了弥补空间信息的不足,引入了来自同一地理区域的另一个较低的空间分辨率DEM。这样,稀疏表示将在轮廓中实现空间约束,并从辅助DEM中提取补充信息。此外,所提出的方法结合了克里格法的无偏估计的优点。为简便起见,所提出的方法称为kriging和稀疏空间正则化(KSSR)方法。 KSSR方法的性能在航天飞机雷达地形任务(SRTM)30 m DEM和先进星载热发射与反射辐射计(ASTER)30 m全球数字高程模型(GDEM)中从相应的轮廓和90米DEM。实验证明,提出的KSSR方法优于传统的克里金法和SSR方法,可以成功地用于从轮廓生成DEM。 (C)2017国际摄影测量与遥感学会(ISPRS)。由Elsevier B.V.发布。保留所有权利。

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