基于ICESat-2 ATLAS数据的DEM高程精度评价

为探究ASTER GDEMV3、SRTM1 DEM和AW3D30 DEM 3种开源DEM数据的高程精度,本文以高精度ICESat-2 ATLAS测高数据为参考数据,利用GIS统计分析、误差相关分析及数理统计对DEM的高程精度进行对比评价。结果表明：①AW3D30的质量最稳定;SRTM1 DEM在平原精度最高;在高原山地精度由高到低依次为AW3D30 DEM、ASTER GDEMV3、SRTM1 DEM。②DEM数据高程精度受地表覆盖影响较大,且与地形因素密切相关,在相同地表覆盖的两个研究区中DEM数据高程精度表现情况不一致,SRTM在平原地表覆盖下精度表现最好,平均误差为3.15 m,AW3D30 DEM在山地地表覆盖下精度表现最好,平均误差为7.61 m。③坡度对DEM数据的高程精度影响较大,在两个研究区3种DEM数据的高程误差均随坡度的增加而增加;坡向对DEM数据的高程精度影响较小,未发现明显的规律。     

露天矿区多源数字高程模型数据对比分析

针对数字高程模型数据源不同会带来一定的不确定性和差异性的问题,选取德国某露天矿为实验区,以高精度DEM数据TanDEM-X为参照,对比了SRTM、AW3D30、ASTER GDEM与TanDEM-X数据的高程精度,分析了DEM数据的差异。结果表明:(1)露天矿区的开采和复垦活动明显地体现在了不同时期获取的DEM高程变化中;(2)在非采矿区,不同DEM数据之间具有较好的一致性,TanDEM-X数据与其他数据的高差均方根误差分别为2.64 m、5.88 m、2.99 m;(3)DEM空间分辨率越高提取得到坡度最值越大,地形描述准确性越高。研究结果为露天矿区DEM应用提供参考。     

SRTM DEM频率域误差特性分析

为利用多源DEM开发出质量更高的DEM,有必要研究数据源的误差特性,本文提出了利用傅里叶变换的多源DEM融合评价数据源的频率误差特性方法。以某实验样区为实验对象,取相同位置的航天飞机雷达地形测绘任务数字高程模型数据(SRTM DEM)与1∶50 000地面高程库数据,并以控制点数据作为参考数据,通过重采样、数据配准、系统误差消除等步骤形成融合数据源,利用傅里叶变换作低通与高通滤波融合,选择不同的截止频率得出不同的融合效果,从而判断SRTM DEM的频率误差特性。实验结果表明SRTM在采用低频信息时,融合效果优于采用高频信息,SRTM的误差更多的表现在高频特性上。     

基于PALSAR数据的DEM提取方法研究

针对PALSAR Level 1.1数据,研究使用NASA/JPL提供的开源干涉软件包ROI_PAC Version 3.0提取DEM.ROI_PAC的目前版本只能处理Level 1.0数据,因此,文章在分析了ROI_PAC软件包处理流程的基础上,提出处理Level 1.1数据的方法,并用PALSAR Level 1.1数据对该方法做了验证.干涉重建DEM与参考DEM的对比结果表明,二者的差异均值为0.27 m,标准差为±9.24 m,80%像元点的高程误差在±10 m以内.     

DEM对高景卫星影像正射校正的影响分析

影像的高精度正射校正需要合适的控制点和DEM,但高分辨率的DEM往往较难获取.鉴于此,利用4种不同尺度的DEM生成高景一号卫星正射影像,并对其精度进行了评价和对比分析,以期为其正射校正实际应用选择何种尺度的DEM提供参考依据.结果表明,在丘陵区域开源DEM可使正射影像平面位置精度的均方根误差优于3 m,而采用5 m格网间距的DEM可使其精度提升至1.5 m.     

DEM约束的卫星影像定位法

作为"云控制"摄影测量理论和方法的发展,研究了DEM约束的立体卫星影像区域网平差方法。与DEM仅作为高程控制信息使用,或者是通过DEM表面匹配实现绝对定向的间接定位方法不同,DEM作为平高控制信息被直接引入至基于RFM模型的卫星影像区域网平差之中。本文方法将连接点地面高程与DEM格网内插高程之差作为虚拟观测值构建约束方程,不仅利用了DEM高程信息,并且利用了其地形曲面包含的平面信息,以"云控制"方式在区域网平差过程中有效消除卫星影像RPC参数中包含的整体偏移及区域网内部的扭曲变形,实现了无地面控制点条件下卫星影像平面及高程绝对定位精度的大幅提升。使用覆盖山东全境的330景天绘一号立体卫星影像进行试验,分别以AW3D30、ASTER GDEM和SRTM GL3共3种开源DEM作为控制信息,并使用100个外业实测控制点进行精度评测。试验表明,以DEM作为控制可显著提高区域网平差的平面与高程精度,卫星影像绝对定位精度与DEM自身精度有关。当使用AW3D30作为控制时,可以取得与使用100个外业控制点平差同等精度,平面中误差为5.0 m(约1像素),高程中误差为2.9 m。试验结果证明了DEM替代外业控制点作为平差控制信息的有效性与可行性。     

困难地区InSAR技术和加权融合的DEM生成

合成孔径雷达干涉测量（InSAR）技术具有全天时全天候的特点，可以大范围、快速、高效地提取DEM。即使在常年被云雾覆盖、降雨频繁的热带雨林地区，InSAR技术也能正常生成DEM。本文利用InSAR生成了研究区的DEM，验证了该方法的可行性。由于研究区植被茂密且有大面积的水域沼泽，导致InSAR处理过程中存在低相干和部分失相干现象，极易造成基于InSAR技术生成的DEM存在错误和空洞。针对这一问题，提出了像素级的以相干依据作为加权函数的融合方法，将SRTM DEM和AW3D30 DEM作为外源数据与InSAR DEM进行融合，解决了基于InSAR技术生成DEM存在错误和空洞的问题，保证了DEM的完整性。     

SRTM(1″)DEM在流域水文分析中的适用性研究

高精度的数字高程模型(digital elevation model,DEM)数据是流域水文分析应用的基础。美国地质调查局新发布了全球高分辨率数字高程数据产品,其空间分辨率为1″(约为30 m)。为评价该数据在流域水文分析中的适用性,以鹤壁汤河流域为实验区,以机载LiDAR DEM数据为参考,统计了SRTM(1″)数据的高程误差,分析了坡度、坡向、地表覆盖等对误差的影响;在基于地形的水文分析中,统计分析了SRTM(1″)数据误差对地形湿度指数、坡度坡长因子以及汇流动力指数等地形指数计算的影响;最后选取流域汇水区面积、最长水流路径长度、形状系数、弯曲度系数等流域特征参数对两种DEM数据提取结果进行了对比。研究表明SRTM(1″)DEM数据具有较高的精度,原始数据均方根误差为5.98 m,在消除平面位移误差后减小为4.32 m。基于地形的水文分析表明SRTM DEM与LiDAR DEM计算结果具有一定的差异,地形湿度指数平均值略高,坡度坡长因子和汇流动力指数平均值偏低,离散度偏小,这与SRTM DEM在微地貌以及高坡度地形区存在失真相关。两种DEM数据提取流域特征参数差异较小。上述研究表明SRTM DEM(1″)数据在流域水文分析中具有较大的应用潜力。     

采用小波分析的SRTM DEM与ASTER DEM数据融合

为了利用航天飞机雷达地形测绘任务数字高程模型(SRTM DEM)与先进星载热反射和反辐射仪数字高程模型(ASTER DEM)的互补信息,提出基于小波分析的多源DEM数据融合方法,以我国秦岭典型高山峡谷地貌类型区为试验样区,选取相同位置的SRTM DEM与ASTER DEM数据,通过重采样、数据配准等步骤形成融合数据源;对小波分解的低频系数作基于邻域像素关联性的融合,高频系数采用像素点绝对值取大的融合,生成融合DEM。并把融合前与融合后的数据分别与1∶5万高程库数据作精度比较,总体统计与抽样检查表明融合DEM精度较源数据均得到了提高。该融合技术为应用SRTM DEM与ASTER DEM生成精度和可靠性更高的DEM产品提供了可行方案。     

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。     

Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points.

Digital Elevation Models (DEMs) contain topographic relief data that are vital for many geoscience applications. This study relies on the vertical accuracy of publicly available latest high-resolution (30?m) global DEMs over Cameroon. These models are (1) the ALOS World 3D-30?m (AW3D30), (2) the Shuttle Radar Topography Mission 1 Arc-Second C-Band Global DEM (SRTM 1) and (3) the Advanced Spaceborne Thermal Emission and Reflection Global DEM Version 2 (ASTER GDEM 2). After matching their coordinate systems and datums, the horizontal positional accuracy evaluation was carried out and it shows that geolocation errors significantly influence the vertical accuracy of global DEMs. After this, the three models are compared among them, in order to access random and systematic effects in the elevation data each of them contains. Further, heights from 555 GPS/leveling points distributed all over Cameroon are compared to each DEM, for their vertical accuracy determination. Traditional and robust statistical measures, normality test, outlier detection and removal were used to describe the vertical quality of the DEMs. The test of the normality rejected the hypothesis of normal distribution for all tested global DEMs. Overall vertical accuracies obtained for the three models after georeferencing and gross error removal in terms of Root Mean Square (RMS) and Normalized Median Absolute Deviation (NMAD) are: AW3D30 (13.06?m and 7.75?m), SRTM 1 (13.25?m and 7.41?m) and ASTER GDEM 2 (18.87?m and 13.30?m). Other accuracy measures (MED, 68.3% quantile, 95% quantile) supply some evidence of the good quality of AW3D30 over Cameroon. Further, the effect of land cover and slope on DEM vertical accuracy was also analyzed. All models have proved to be worse in the areas dominated by forests and shrubs areas. SRTM 1 and AW3D30 are more resilient to the effects of the scattering objects respectively in forests and cultivated areas. The dependency of DEMs accuracy on the terrain roughness is evident. In all slope intervals, AW3D30 is performing better than SRTM 1 and ASTER GDEM 2 over Cameroon. AW3D30 is more representative of the external topography over Cameroon in comparison with two others datasets and SRTM 1 can be a serious alternative to AW3D30 for a range of DEM applications in Cameroon.     

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.     

DEM matching for bias compensation of rigorous pushbroom sensor models

DEM matching is a technique to match two surfaces or two DEMs, at different reference frames. It was originally proposed to replace the need of ground control points for absolute orientation of perspective images. This paper examines DEM matching for precise mapping of pushbroom images without ground control points. We proved that DEM matching based on 3D similarity transformation can be used when model errors are only on the platform’s position and attitude biases. We also proposed how to estimate bias errors and how to update rigorous pushbroom sensor models from DEM matching results. We used a SPOT-5 stereo pair at ground sampling distance of 2.5 m and a reference DEM dataset at grid spacing of 30 m and showed that rigorous pushbroom models with accuracy better than twice of the ground sampling distance both in image and object space have been achieved through DEM matching. We showed further that DEM matching based on 3D similarity transformation may not work for pushbroom images with drift or drift rate errors. We discussed the effects of DEM outliers on DEM matching and automated removal of outliers. The major contribution of this paper is that we validate DEM matching, theoretically and experimentally, for estimating position and attitude biases and for establishing rigorous sensor models for pushbroom images.     

Urban DEM generation,analysis and enhancements using TanDEM-X

This paper analyzes the potential of the TanDEM-X mission for the generation of urban Digital Elevation Models (DEMs). The high resolution of the sensors and the absence of temporal decorrelation are exploited. The interferometric chain and the problems encountered for correct mapping of urban areas are analyzed first. The operational Integrated TanDEM-X Processor (ITP) algorithms are taken as reference. The ITP main product is called the raw DEM. Whereas the ITP coregistration stage is demonstrated to be robust enough, large improvements in the raw DEM such as fewer percentages of phase unwrapping errors, can be obtained by using adaptive fringe filters instead of the conventional ones in the interferogram generation stage. The shape of the raw DEM in the layover area is also shown and determined to be regular for buildings with vertical walls. Generally, in the presence of layover, the raw DEM exhibits a height ramp, resulting in a height underestimation for the affected structure. Examples provided confirm the theoretical background. The focus is centered on high resolution DEMs produced using spotlight acquisitions. In particular, a raw DEM over Berlin (Germany) with a 2.5 m raster is generated and validated. For this purpose, ITP is modified in its interferogram generation stage by adopting the Intensity Driven Adaptive Neighbourhood (IDAN) algorithm. The height Root Mean Square Error (RMSE) between the raw DEM and a reference is about 8 m for the two classes defining the urban DEM: structures and non-structures. The result can be further improved for the structure class using a DEM generated with Persistent Scatterer Interferometry. A DEM fusion is thus proposed and a drop of about 20% in the RMSE is reported.     

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。     

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.