| 顾及数据配准的江西省SRTM DEM精度评价和修正 |
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| 引用本文： | 杨帅,杨娜,陈传法,常兵涛,高原,郑婷婷.顾及数据配准的江西省SRTM DEM精度评价和修正[J].地球信息科学,2021,23(5):869-881. |
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| 作者姓名： | 杨帅 杨娜 陈传法 常兵涛 高原 郑婷婷 |
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| 作者单位： | 1.山东科技大学测绘与空间信息学院,青岛 2665902.济南市勘察测绘研究院,济南 250101 |
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| 基金项目： | 国家自然科学基金项目(41804001);国家自然科学基金项目(41371367);山东省自然科学基金项目(ZR2020YQ26);山东省自然科学基金项目(ZR2019MD007);山东省自然科学基金项目(ZR2019BD006);山东省高等学校青创科技支持计划(2019KJH007) |
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| 摘 要： | 目前,ICESat/GLAS是大尺度SRTM DEM精度评价的主要数据源。然而,现有的精度评价方法均忽略了2组数据的有效配准。为此,本文分析了数据配准前、后SRTM DEM整体精度差异,以及不同地形因子和土地利用类型对SRTM DEM影响程度。在此基础上,充分考虑SRTM DEM精度影响因素,分别借助多元线性回归（MLR）、后向传播神经网络（BPNN）、广义回归神经网络（GRNN）以及随机森林（RF）对SRTM DEM修正。结果分析表明：配准前,ICESat/GLAS与SRTM DEM沿x、y方向的平均水平位移分别为-17.588 m、-29.343 m,高程方向系统偏差为-2.107 m;配准后,SRTM DEM的系统误差基本消除,而且中误差降低了14.4%。配准前,坡向与SRTM DEM误差呈正弦函数关系,配准后这种关系基本消失。SRTM DEM误差均随地形起伏度、坡度、高程的增加呈增大趋势; 6种土地利用类型中,SRTM DEM在林地误差最大,未利用土地误差最小。对配准后SRTM DEM修正表明,RF效果最优,其中误差分别比MLR、BPNN、GRNN降低了3.1%、2.7%、11.3%。
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| 关 键 词： | SRTM ICESat/GLAS 数据配准 精度 线性回归 随机森林 DEM 修正 地形因子 土地利用类型 |
| 收稿时间： | 2020-07-25 |
Accuracy Assessment and Improvement of SRTM DEM based on ICESat/GLAS under the Consideration of Data Coregistration over Jiangxi Province |
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| YANG Shuai,YANG Na,CHEN Chuanfa,CHANG Bingtao,GAO Yuan,ZHENG Tingting.Accuracy Assessment and Improvement of SRTM DEM based on ICESat/GLAS under the Consideration of Data Coregistration over Jiangxi Province[J].Geo-information Science,2021,23(5):869-881. |
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| Authors: | YANG Shuai YANG Na CHEN Chuanfa CHANG Bingtao GAO Yuan ZHENG Tingting |
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| Institution: | 1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China2. Jinan Geotechnical Investigation and Surveying Institute, Jinan 250101, China |
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| Abstract: | At present, ICESat/GLAS has become the main data source for large-scale SRTM DEM accuracy assessment. Nevertheless, almost all the existing methods neglected the effective coregistration of the two datasets. In order to evaluate the importance of data coregistration, this paper took Jiangxi Province as the research area and analyzed the overall accuracy of SRTM DEM before and after data coregistration. Results show that after data coregistration, the Mean Bias (ME) of SRTM DEM was eliminated significantly, and the DEM Root Mean Square (RMSE) was reduced by 14.4%. We further analyzed the effect of terrain factors (terrain slope, terrain aspects, terrain relief, elevation) and land use types on the accuracy of SRTM DEM. Specifically, this study area was divided into different sub-regions according to slope ranges (0~5°, 5~10°, 10~15°, 15~20°, >20°), aspect ranges (-1, 0~22.5°, 22.5~67.5°, 67.5~112.5°, 112.5~157.5°, 157.5~202.5°, 202.5~247.5°, 247.5~292.5°, 292.5~337.5°, 337.5~360°), relief ranges and elevation ranges (0~100 m, 100~200 m, 200~300 m, >400 m), and land use types (cultivated land, forest cover, grassland, water area, built-up area, unused land), respectively. Then, the ME and RMSE of each sub-region were computed and analyzed. We found that the terrain aspects with a sine-like shape were strongly related to SRTM DEM errors before data coregistration; however, this relationship basically disappeared after data coregistration. The SRTM DEM errors increased with the increase of terrain relief, slope, and elevation. Among the six land use types, SRTM DEM had different accuracy under different land use types. More specifically, SRTM DEM had the highest accuracy on unused land and the lowest accuracy on forest land. Finally, by incorporating terrain slope, aspect, terrain relief, elevation, land use, and ICESat/GLAS data randomly selected with the proportion of 90% into the revision models, the SRTM DEM was improved by use of Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), Generalized Regression Neural Network (GRNN), and Random Forest (RF), respectively. Accuracy evaluation of corrected SRTM DEM by use of the remaining 10% ICESat/GLAS data demonstrated that the four correction models with data coregistration obviously outperform themselves without the coregistration. Among the four corrected models, RF produced the best result while GRNN produced the worst result. The RMSE of RF was about 3.1%, 2.7%, and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Therefore, RF was finally selected to enhance accuracy of SRTM DEM. |
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| Keywords: | SRTM ICESat/GLAS data coregistration accuracy linear regression Random forest DEM enhancement terrain factors land use types |
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