我国数字高程模型与数字地形分析研究进展

融合多源数字高程模型(digital elevation model, DEM)数据能有效利用数据间的互补优势，提升单一源数据的质量。提出一种基于深度置信网络(deep belief networks, DBN)的点面融合模型，在DBN的框架下考虑地形坡度、地表覆盖和空间位置信息等因素对DEM高程误差空间分布的影响，建立DEM高程值与高精度激光雷达测高数据之间的回归关系，从而实现多源栅格DEM与激光雷达测高点数据的空间融合，提升栅格DEM的垂直精度。对于空洞数据，根据空洞和非空洞区域的范围建立相应的输入数据集，分别进行融合，再利用不规则三角网差分曲面方法实现融合结果的无缝拼接。实验结果表明，相比原始DEM数据和两两融合的结果，所提出的多源DEM点面融合模型能够大幅度提升数据精度，有效解决原始数据中存在的空洞、噪声和异常值等问题。

Progress of DEM and Digital Terrain Analysis in China

  Objectives  Although there have been a variety of global digital elevation model (DEM) products available for geospatial applications, the data quality of DEMs are inevitably affected by the observing techniques and processing methods. Multi-source data fusion by integrating the complementary information among the datasets is an effective way to improve DEM data quality. Based on the facts, this paper proposed a point-surface fusion model based on deep belief network (DBN).  Methods  The terrain slope, land cover, and the spatial coordinates were integrated into the DBN model, thus constructed the statistical relation between the DEM elevations and the high-quality ICESat (ice, cloud, and land elevation) GLAS (geoscience laser altimeter system) data. The DBN-based fusion model was capable to learn the spatial structure and landscape associations of the DEM errors, and thus the multi-source raster DEMs and the high-quality LiDAR altimetry can be effectively fused. For the data tiles with voids, the triangulated irregular network (TIN) delta surface method was employed to merge the fusion results for the void and non-void areas, respectively.  Results  In the experiments, the effectiveness of the proposed method is tested on the fusion of ASTER (advanced spaceborne thermal emission and reflection radiometer) GDEM (global digital elevation model) v2, ALOS AW3D30 (advanced land observing satellite world 3D-30 m) and SRTM1 (shuttle radar topography mission 1-arc second) data, which are the most popular DEM products. Six data tiles with different terrain conditions are tested, and the accuracy of the original data and the fusion results are evaluated using the ICESat GLAS points with cross-validation. Quantitative results show that the fused data have higher accuracy compared with the original datasets and the pair-wise fusion results, both in terms of the void areas and the non-void areas.  Conclusions  The results show that the proposed method is able to improve the accuracy of the DEM data with fusion of multi-source altimetry datasets. Moreover, it is effective in dealing with the data voids, noise and abnormal values simultaneously.