DEM Fusion using a modified k-means clustering algorithm

Digital elevation models (DEMs) are a necessary dataset for modelling the Earth’s surface; however, all DEMs contain error. Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be available for a region. However, the use of a clustering algorithm in DEM fusion has not been previously reported. In this study a new DEM fusion algorithm based on a clustering approach that works on multiple DEMs to exploit consistency in the estimates as indicators of accuracy and precision is presented. The fusion approach includes slope and elevation thresholding, k-means clustering of the elevation estimates at each cell location, as well as filtering and smoothing of the fusion product. Corroboration of the input DEMs, and the products of each step of the fusion algorithm, with a higher accuracy reference DEM enabled a detailed analysis of the effectiveness of the DEM fusion algorithm. The main findings of the research were: the k-means clustering of the elevations reduced the precision which also impacted the overall accuracy of the estimates; the number of final cluster members and the standard deviation of elevations before clustering both had a strong relationship to the error in the k-means estimates.     

Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT-1 DEM: a case study of Shahjahanpur district,Uttar Pradesh,India

Digital elevation model (DEM) data of Shuttle Radar Topography Mission (SRTM) are distributed at a horizontal resolution of 90 m (30 m only for US) for the world, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data provide 30 m horizontal resolution, while CARTOSAT-1 (IRS-P5) gives 2.6 m horizontal resolution for global coverage. SRTM and ASTER data are available freely but 2.6 m CARTOSAT-1 data are costly. Hence, through this study, we found out a horizontal accuracy for selected ground control points (GCPs) from SRTM and ASTER with respect to CARTOSAT-1 DEM to implement this result (observed from horizontal accuracy) for those areas where the 2.6-m horizontal resolution data are not available. In addition to this, the present study helps in providing a benchmark against which the future DEM products (with horizontal resolution less than CARTOSAT-1) with respect to CARTOSAT-1 DEM can be evaluated. The original SRTM image contained voids that were represented digitally as ?140; such voids were initially filled using the measured values of elevation for obtaining accurate DEM. Horizontal accuracy analysis between SRTM- and ASTER-derived DEMs with respect to CARTOSAT-1 (IRS-P5) DEM allowed a qualitative assessment of the horizontal component of the error, and the appropriable statistical measures were used to estimate their horizontal accuracies. The horizontal accuracy for ASTER and SRTM DEM with respect to CARTOSAT-1 were evaluated using the root mean square error (RMSE) and relative root mean square error (R-RMSE). The results from this study revealed that the average RMSE of 20 selected GCPs was 2.17 for SRTM and 2.817 for ASTER, which are also validated using R-RMSE test which proves that SRTM data have good horizontal accuracy than ASTER with respect to CARTOSAT-1 because the average R-RMSE of 20 GCPs was 3.7 × 10^?4 and 5.3 × 10^?4 for SRTM and ASTER, respectively.     

多源星载SAR地形干涉测量精度分析

星载InSAR技术具有高效率、高精度获取全球DEM数据及其他增值产品的优势，是地形测绘领域的研究热点。本文综合利用国内外多源星载SAR影像数据，开展地形干涉测量试验及精度对比分析，旨在为全球测绘提供技术参考。现有的X/C/L波段卫星SAR系统，如COSMO-SkyMed、GF-3、ALOS-2获取的干涉数据集，在青海典型复杂地形实验区均成功获取了DEM数据产品。分析结果表明，在3种典型数据源中，基于COSMO-SkyMed干涉测量DEM的精度与细节质量相对较高，GF-3干涉结果次之，ALOS-2数据也实现了较好的地形测图精度。相关结果从侧面论证了国产GF-3数据具备在空间基线合适的条件下获得高精度DEM数据产品的潜力。     

基于SRTM DEM的InSAR高分辨率山区地表高程重建算法

山体的叠掩和阴影现象造成的信号去相关,一直是InSAR重建山区地表高程的瓶颈之一.为此,提出了一种新的基于粗分辨率SRTM DEM(约90m分辨率)辅助InSAR数据重建山区地表高程的方法.利用SRTM DEM模拟的干涉相位,对ERS-1/2干涉相位做去地形相位处理,得到残余相位.通过对解缠后的残余相位计算方差提取叠掩和阴影区域的噪声,并用平均相位近似恢复噪声区域的相位,然后将其转换为高程,并用SRTM DEM作高程补偿处理,从而实现地表高程重建.最后,定量比较了该方法与传统InSAR技术生成的DEM精度.实验表明,这种方法能有效提高传统InSAR技术生成地表高程的精度,这对提高星载雷达数据的使用效率具有重要意义.     

Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.     

Preparing The National Map for the 3D Elevation Program – products,process and research

The 3D Elevation Program (3DEP) is a collaborative effort among government entities, academia, and the private sector to collect high-resolution 3-dimensional data over the United States. The United States Geological Survey (USGS) is making preparations for managing, processing, and delivering petabytes of 3DEP elevation products for the Nation. In addition to the existing 1/3, 1, and 2 arc-second seamless elevation data layers of The National Map, new 3DEP products include lidar point cloud data; a standard 1-meter DEM layer; additional source datasets; and, in Alaska, 5-meter digital elevation models. A new product generation system improves the construction and publication of the seamless elevation datasets, prepares the additional 3DEP products for distribution, and automates the data management functions required to accommodate the high-volume 3DEP data collection. Major changes in geospatial data acquisition, such as high resolution lidar data, volunteered geographic information, data processing using parallel and grid computer systems, and user needs for semantic access to geospatial data and products, are driving USGS research associated with the 3DEP. To address the research requirements, a set of inter-related projects including spatiotemporal data models, data integration, geospatial semantics and ontology, high performance computing, multi-scale representation, and hydrological modeling using lidar and other 3DEP data has been developed.     

ENVISAT/ASAR多角度干涉雷达数据山区DEM生成及精度分析

在地形起伏较大的山区用干涉SAR生成DEM时,合适的入射角是获取高精度DEM的重要参数之一.本文基于ENVISAT/ASAR多角度干涉雷达数据,SRTM 90米分辨率的DEM以及1:5万的数字化DEM数据,从定性和定量的角度比较和分析了干涉SAR在获取山区DEM时,入射角对DEM精度的具体影响.结果表明:对于ASAR的多入射角干涉雷达数据,在不同入射角条件下由于雷达叠掩和透视收缩的影响,获取的DEM精度差别很大,入射角带来的影响相当显著,比如IS2和IS4角度得到的DEM的精度差超过了6米.因此,在山区干涉SAR地形成图时,必须对入射角的大小进行严格的选择.     

插值条件下格网DEM坡度计算模型的噪声误差分析

针对目前DEM（Digital Elevation Model, DEM）数字地形分析的精度评价多数不考虑DEM误差的空间自相关性或仅仅采用经验的自相关性模型问题,本文从DEM插值入手,从理论上推导了插值条件下格网DEM邻域窗口内坡度噪声误差的空间自相关性模型以及坡度精度模型,并选取典型的插值方法和坡度差分算法,从实验角度分析了在顾及和不顾及空间自相关性两种情况下的格网DEM坡度计算模型的噪声精度,实验结果表明：坡度精度受DEM噪声误差的空间自相关性影响较大,并与DEM插值方法和坡度计算模型中的差分算法有关。     

An improved ANUDEM method combining topographic correction and DEM interpolation

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The experiments undertaken confirmed the superiority of the proposed method over the other DEM interpolation methods. It is shown that the proposed method can provide results with a higher accuracy, as well as a better visual quality.     

光学航天传感器几何建模与DEM生成新进展

高分辨率光学卫星成像系统正越来越多地应用于遥感和摄影测量领域。根据新型的高分辨率卫星影像系统主要特点及高分辨率卫星测图的关键技术的发展趋势，以第21届国际摄影测量与遥感大会“光学航天传感器几何建模与DEM生成”专题中的51篇文章为参考，总结了目前典型的光学卫星影像（主要是IRS—P5和ALOS／PRISM）的几何建模方法，影像匹配与DEM提取方法以及光学航天传感器在其他方面的应用。     

关于DEM数据质量检查内容和方法的探讨

生产方式、资料精度、生产人员素质都会影响DEM数据的最终质量。本文结合多年的DEM数据质量检查工作,从检查内容出发,结合检查方法探讨DEM的质量控制。并在此基础上给出了ArcGIS平台下的检查方法,提出了适合程序实现的高程异常值探测新方法,有助于提高DEM数据质量检查的效率,对生产单位在DEM数据生产过程中避免出现一些技术问题有一定的借鉴作用。     

基于嫦娥二号立体影像的全月高精度地形重建

提出了一种针对嫦娥二号高分辨率立体影像的全月处理方法,采用5个激光反射器的月面位置作为绝对控制点,制作了一个新的全月地形产品（CE2TMap2015）,包括7 m、20 m和50 m 3种不同分辨率类型的数据,实现了全月球范围内的无缝镶嵌和高精度绝对定位,数字高程模型（DEM）和数字正射影像（DOM）均实现了全月球覆盖。在评估CE2TMap2015地形数据产品平面位置精度和高程精度的基础上,与SLDEM2013、GLD100和LOLA DEM等全月地形产品进行了比较,结果显示CE2TMap2015地形数据产品在空间分辨率、全月覆盖率、数据连续性、绝对定位精度和地貌结构细节表达等方面与其他全月地形产品相比具有明显的优势,可广泛用于月球科学研究和应用。     

结合NASA DEM和AW3D30 DEM的太原市DEM数据融合

本文首先以DEM数据为例,将输入数据与参考数据配准至同一像素位置,然后分别将均方根误差和标准差作为参考指标,在不同坡度等级的区域内,通过权重系数从0至1的遍历探寻最佳加权融合系数,从而确定融合方案并进行NASA DEM与AW3D30 DEM的数据融合,最后对融合效果进行定量评价。结果表明:配准前,NASA DEM沿x、y、z方向的位移分别为-2.65、2.41、0.60m,AW3D30 DEM位移分别为1.04、7.51、-3.33m;配准后,原始数据各项误差均减小,且NASA DEM的系统误差基本消失。融合DEM相较于NASA DEM,平均误差和均方根误差分别减小了25.0%和36.8%;对于AW3D30 DEM,误差降幅分别为86.5%和13.2%。     

结合GPS和GLAS数据生成Dome-A区域DEM

详细阐述了利用GLAS数据和GPS数据生成Dome-A地区DEM的方法。首先进行GLAS数据转化, 便于与GPS数据结合, 提出一种快速搜索GLAS和其光斑(Footprint)覆盖GPS点的算法, 比较GLAS数据和GPS数据发现, 均值差异最大为1.118 m, 最小为0.997 m, 而标准差稳定为5-6 cm, 在进行椭球变换修正之后, 差值最大为0.405 m, 最小为0.284 m;之后利用改进的角度限差法沿测线对GPS数据进行特征点提取, 得到抽稀之后的数据;再利用抽稀之后的GPS数据和处理后的GLAS数据使用克里金插值方法生成研究区DEM。利用1199个GPS点和53个GLAS检验点对最后生成的DEM进行了精度分析, 残差中误差为5 cm, 最大残差绝对值为12 cm。利用原始GPS数据, 原始GPS数据和GLAS数据, 处理后GPS数据利用克立金插值方法分别生成了研究区的DEM, 通过等高线提取分析以及检验点的误差分析, 处理后的GPS数据生成的DEM要优于原始GPS数据的, 证明GPS处理的必要性。     

Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)

Digital Elevation Model (DEM) is a quantitative representation of terrain and is important for Earth science and hydrological applications. DEM can be generated using photogrammetry, interferometry, ground and laser surveying and other techniques. Some of the DEMs such as ASTER, SRTM, and GTOPO 30 are freely available open source products. Each DEM contains intrinsic errors due to primary data acquisition technology and processing methodology in relation with a particular terrain and land cover type. The accuracy of these datasets is often unknown and is non-uniform within each dataset. In this study we evaluate open source DEMs (ASTER and SRTM) and their derived attributes using high postings Cartosat DEM and Survey of India (SOI) height information. It was found that representation of terrain characteristics is affected in the coarse postings DEM. The overall vertical accuracy shows RMS error of 12.62 m and 17.76 m for ASTER and SRTM DEM respectively, when compared with Cartosat DEM. The slope and drainage network delineation are also violated. The terrain morphology strongly influences the DEM accuracy. These results can be highly useful for researchers using such products in various modeling exercises.     

基于LROC NAC影像的高分辨率连续DEM数据制作

具有高分辨率和连续表面的DEM数据是获取月球形貌特征并进行数字地形分析的主要数据源。本文选择嫦娥五号候选着陆区中的一个区域作为试验区,首先,基于LROC NAC立体影像、ISIS3和Stereo Pipeline软件生成高分辨率DOM影像及对应DEM数据,并将其与日本SELENE数据进行对比;然后,利用反距离权重、径向基函数和经验贝叶斯克里金3种插值方法对DEM数据的空洞区域进行修复,并对不同修复方法进行交叉验证分析。结果表明：生成的DOM和DEM分辨率约3.5 m,明显比7.4 m分辨率的日本SELENE数据清晰,并具有更强的地形表达能力;径向基函数插值法的空洞修复效果最好,交叉验证均方根误差为0.26 m。本文对准确获取月球形貌特征、探测器选址等具有一定作用,并能够为其他区域的高分辨率连续DEM数据生成提供参考。     

多源DEM融合的高差拟合神经网络方法

本文侧重于介绍智能化摄影测量机器学习的高差拟合神经网络方法。观测手段和处理方式等限制导致全球高质量无缝DEM数据的缺乏,进而制约了它在水文、地质、气象及军事等领域的应用。本文提出了一种基于高差拟合神经网络的多源DEM融合方法,尝试融合全球DEM产品SRTM1、ASTER GDEM v2和激光雷达测高数据ICESat GLAS。首先,根据ICESat GLAS的相关参数及与DEM数据的高程差值,结合坡度自适应的思想设置高差阈值对ICESat GLAS进行滤波,剔除异常数据点。然后,以ICESat GLAS数据为控制点,利用神经网络模型拟合ASTER GDEM v2的误差分布。以地形坡度信息和经纬度坐标作为网络输入,ICESat GLAS和ASTER GDEM v2的高程差值作为目标输出,训练得到预测高差,将其与ASTER GDEM v2高程值相加即可获得校正结果。最后,引入TIN差分曲面的方法,利用校正后的ASTER GDEM v2高程值对SRTM1的数据空洞进行填充,融合生成空间无缝DEM。本文通过随机选取数据进行真实试验,对模型进行了精度验证,并给出了处理结果的定量评价和目视效果。结果表明,不论是空洞还是整体区域,本文方法相比其他DEM数据集和其他方法的处理结果都能够在RMSE上表现出优势,同时,本文提出的方法能够有效克服ASTER GDEM中异常值的影响,得到空间无缝DEM。     

不同格网密度的DSM/DEM对影像分类精度的影响研究

在无控制点的卫星影像正射校正中,大多采用DSM/DEM数据作为辅助数据来消除或限制因地形起伏引起的形变,然而经不同格网密度的DSM/DEM正射校正后的影像对后续处理会产生不同程度的影响,如对地物分类精度产生影响。针对这一问题,本文分别采用不同的DSM/DEM数据(China DSM 15 m、ASTER GDEM 30 m和SRTM 90 m)对资源三号影像进行正射校正,然后对正射校正后影像利用支持向量机进行分类,比较正射校正后影像结果的分类精度。结果表明:在相同重采样方法下,影像经China DSM 15 m DSM正射校正后结果的分类精度优于ASTER GDEM 30 m DEM和SRTM 90 m DEM。     

数字高程模型(DEM)成果裁切探讨

随着计算机技术及测绘产品的不断发展,数字高程模型(DEM)已成为地理信息空间系统和"数字地球"的重要组成部分。在测绘技术蓬勃发展的今天,数字高程模型(DEM)的生产已成为各生产部门较关注的问题之一,特别是近年来数字龙江地理空间框架建设一期工程对数字高程模型(DEM)产品成果的要求有很大提高,数字高程模型(DEM)成果的裁切也显得尤为重要,本文从工作中遇到的DEM成果裁切问题入手,结合笔者多年的测绘生产经验,研讨DEM成果裁切问题。     

DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions

It is viable to differentiate the deep and shallow flood inundated regions through a new flood feature extraction techniques named as ‘Digital Elevation Model (DEM) and Synthetic Aperture Radar (SAR) image based flood feature extraction model’. The proposed model has been built mainly on the top of DEM of the disaster region without adopting standard multi-layer GIS techniques. To meet the time related factors of flood early warning system the image clustering operations has been automated at three different levels which bifurcates the input datasets and extracts the much required end results such as deep flooded regions, shallow flood inundated regions and non-flooded regions. The model has been tested with SAR flood images of known geographical region as well as remote geographical region. The proposed model can be automated against the input SAR sensor image and corresponding DEM of the respective SAR scene of any part of the world.