| 基于开源数据和条件生成对抗网络的地形重建方法 |
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| 引用本文： | 陈凯,雷少华,代文,王春,刘爱利,李敏.基于开源数据和条件生成对抗网络的地形重建方法[J].地球信息科学,2023,25(2):252-264. |
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| 作者姓名： | 陈凯 雷少华 代文 王春 刘爱利 李敏 |
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| 作者单位： | 1.南京信息工程大学 地理科学学院，南京 2100442.实景地理环境安徽省重点实验室，滁州 2390003.南京水利科学研究院 水文水资源与水利工程科学国家重点实验室，南京 210029 |
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| 基金项目： | 江苏省高等学校自然科学研究项目(22KJB170016);国家自然科学基金项目(42101384);江苏省自然科学基金青年基金项目(BK20210043);安徽高校省级自然科学研究重大项目(KJ2021ZD0130);江西省水利厅重大科技项目(202124ZDKT29);“实景地理环境安徽省重点实验室”开放课题资助(2022PGE013) |
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| 摘 要： | 如何使用少量的地形特征复原地形地貌一直为地学领域的难题。本文使用开源数据集提取地形特征要素,使用地形特征要素作为约束条件构建了用于生成DEM的条件生成对抗网络(Conditional Generative Adversarial Networks, CGAN),设计了基于开源DEM、开源DEM与遥感影像组合、以及5m高精度DEM提取地形特征要素生成DEM的对比实验,并对结果进行视觉效果、相关性分析以及地形因子的对比与评价。结果表明:(1)在视觉效果上,3种不同方式生成的DEM在视觉效果上均十分逼近原始5 m DEM,都远好于传统插值方法生成DEM,基于开源12.5m DEM提取要素和1m遥感影像的重建效果最接近于原始5 m DEM;(2)在相关性上,三种不同方式生成的DEM与原始5m DEM相关性均能达到0.75以上,组合开源数据提取要素重建DEM与原始5 m DEM相关性可达到0.85以上;(3)在地形因子方面,基于开源12.5 m DEM和1 m遥感影像提取要素重建DEM的坡度和坡向的分布趋势与原始5 m DEM最为一致。本文为高精度DEM建模提供了新的思路,在高精度DEM难以获取...
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| 关 键 词： | 开源数据 遥感影像 条件生成对抗网络 数字高程模型 地形特征要素 地形重建 地形骨架 黄土高原 |
| 收稿时间： | 2022-09-19 |
Terrain Rebuilding Method based on Open Source Data and Conditional Generative Adversarial Networks |
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| CHEN Kai,LEI Shaohua,DAI Wen,WANG Chun,LIU Aili,LI Min.Terrain Rebuilding Method based on Open Source Data and Conditional Generative Adversarial Networks[J].Geo-information Science,2023,25(2):252-264. |
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| Authors: | CHEN Kai LEI Shaohua DAI Wen WANG Chun LIU Aili LI Min |
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| Institution: | 1. School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China2. Key Laboratory of Physical Geographic Information in Anhui Province, Chuzhou 239000, China3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China |
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| Abstract: | How to use a small number of topographic features to restore the topography has been a difficult problem in the field of geology. In this paper, we extract topographic features from open source datasets, and construct Conditional Generative Adversarial Networks (CGAN) for DEM generation using topographic features as constraints, a comparative experiment was designed based on the combination of open-source DEM, open-source DEM and remote sensing image, as well as the generation of DEM by extracting topographic features from the high-precision DEM with a resolution of 5 m, the results were compared and evaluated by visual effect, correlation analysis and topographic factors. The results show that: (1) in the visual effect, the DEM generated by three different methods are very close to the original DEM with a resolution of 5m, which is much better than the traditional interpolation method, (2) the correlation between DEM generated by three different methods and the original DEM with a resolution of 5m is more than 0.75, and the result of reconstruction based on dem with a resolution of 5 m extracted from open source and remote sensing image with a resolution of 1m is closest to that of the original DEM with a resolution of 5m, the correlation between DEM and original 5m DEM can reach more than 0.85. (3) in the aspect of terrain factor, based on dem with a resolution of 5 m and remote sensing image with a resolution of 1m, the distribution trend of slope and aspect of reconstructed DEM is most consistent with the original DEM with a resolution of 5 m. This paper provides a new idea for high-precision DEM modeling. In the areas where high-precision DEM is difficult to obtain, high-precision terrain modeling can be carried out by using open source data sets and Conditional Generative Adversarial Networks, so as to conduct geoscience analysis and geographical simulation. |
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| Keywords: | open source data remote sensing images conditional generative adversarial networks Digital Elevation Model topographic features topographic rebuilding terrain skeleton Loess Plateau |
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