一种基于人工神经网络和插值技术的数字高程模型合并方法

Mustafa K. Alemam, Bin YONG, Abubakar Sani-Mohammed
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摘要

数字高程模型(DEM)是地形高程最重要的来源之一,在各种地球科学应用中都是必不可少的。大多数这些应用都需要精确的标高,这需要很高的成本。因此,像航天飞机雷达地形任务(SRTM) DEM这样的数据来源是所有用户经常可以访问的,但精度很低。因此,许多研究试图提高从这些免费来源获得的dem的准确性。重要的是,对于部分包含高精度数据的区域,不建议使用SRTM DEM。因此,需要一种合并技术,以提高精度产生整个区域的合并DEM。近年来,地理信息系统(GIS)的进步通过提供应用合并技术(如最小、最大、最后、第一、平均和混合(传统方法))的工具来改进dem,从而改进了数据分析。本文提出了基于人工神经网络和插值技术的DEM合并方法。并与商业GIS软件中已有的方法进行了比较。采用克里格插值法、逆距离加权插值法和样条插值法。实现合并阶段的关键步骤是生成校正面,用于修改SRTM DEM。此外,还考虑了零边界和H边界两种情况。结果表明,所提出的DEM合并方法(PDMMs)比传统方法(cdmm)更能提高SRTM DEM的精度。研究结果进一步表明,H边界的PDMMs比零边界的PDMMs获得了更高的精度,而kriging在这两种情况下都优于其他插值方法。人工神经网络以最高的准确率优于所有方法。零边和H边海拔分别提高22.38%和75.73%,坡度分别提高34.67%和54.83%,坡向分别提高40.28%和52.22%。因此,这种方法将具有成本效益,特别是在关键的工程项目中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy
ABSTRACT The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.
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