Multiscale DEM generation on basis of singular value decomposition

Caixian Zhang, Jun He, Wenguang Hou
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引用次数: 1

Abstract

As the fundamental data about the terrains, DEM plays an important role in many fields. The high resolution DEM is increasingly popular. Yet, the multiscale resolution DEMs are still desired for some applications due to the fact that the low resolution DEM can reduce the memory demands with limited computational complexity. Then, how to obtain the multiscale DEMs remains an open question, which demands that the different resolution DEMs should discard the detailed information with maintaining the main information of the high resolution DEM. Moreover, the multiscale DEMs should not cost many memories. Generally, there is a contradiction. As such, this paper proposes a multiscale DEM generation method based on Singular Value Decomposition (SVD) which can establish multiscale DEMs maintaining the different details with a small quantity of memory increasement. The method is simple but effective. Lots of experiment shows its effectiveness.
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基于奇异值分解的多尺度DEM生成
对地形的基本数据,民主党在许多领域扮演着重要的角色。高分辨率DEM越来越受欢迎。然而,由于低分辨率DEM可以在有限的计算复杂度下减少内存需求,因此在某些应用中仍然需要多尺度分辨率DEM。那么,如何获得多尺度DEM仍然是一个有待解决的问题,这就要求不同分辨率DEM在保留高分辨率DEM的主要信息的同时,放弃细节信息。此外,多尺度dem不需要占用太多内存。一般来说,这是一个矛盾。为此,本文提出了一种基于奇异值分解(SVD)的多尺度DEM生成方法,该方法可以在少量内存增量的情况下建立保持不同细节的多尺度DEM。这个方法简单而有效。大量实验证明了该方法的有效性。
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