Digital Elevation Model Extraction Research Articles

Evaluation of high‐resolution DEMs from satellite imagery for geomorphic applications: A case study using the SETSM algorithm

AbstractHigh‐resolution digital elevation models (DEMs) have revolutionized research in geomorphology by allowing for detailed quantitative analysis of Earth's surface. Satellite stereo images offer the promise of expanding the availability of high‐resolution DEMs over broad areas, but rigorous evaluation of the scientific application of these datasets remains limited. In this study, we consider DEMs built using stereo pairs of high‐resolution (0.5 m) satellite imagery and the open‐source DEM extraction algorithm, Surface Extraction from TIN Space‐search Minimization (SETSM). We selected locations across a range of landscapes to evaluate the application of these DEMs to geomorphic problems, with particular attention to hillslope analyses where high spatial resolution has been shown to be important for revealing topographic signatures of tectonic and environmental processes. We compared the quality of SETSM 2 m DEMs to LiDAR‐derived DEMs and the widely available SRTM‐30 m and ALOS‐30 m DEMs by comparing the elevation data and derivative products (e.g., slope, aspect, and curvature). We found that SETSM DEMs performed noticeably better than SRTM and ALOS DEMs, but with systematic biases relative to LiDAR DEMs in regions with vegetation. Moreover, noise in the initial SETSM elevation data is amplified with every subsequent derivative, significantly decreasing quality. Finally, we evaluated the potential use of SETSM products for change detection. Applying DEM differencing to a major landslide, we found volume and sediment thickness from SETSM DEMs were similar to volumes and thicknesses from other studies. This example illustrates the capabilities of SETSM and other satellite‐based stereo‐photogrammetry for contributing to rapid response after natural disasters. Overall, we conclude that DEMs derived from satellite image stereo‐photogrammetry can markedly improve on lower resolution global elevation data for terrain analysis and can open possibilities for change detection, but that care needs to be taken in their application especially in regions with significant vegetation.

A Novel DEM Extraction Method Based on Chain Correlation of CSAR Subaperture Images

Circular synthetic sperture radar (CSAR) can observe the target in 360° and obtain the target's total scattering information. By dividing the CSAR imaging data into subapertures, the target digital elevation model (DEM) information can be directly obtained. Aiming at the problems existing in the division and selection of subapertures when acquiring DEM, this article proposes a chain correlation method. First, the echo data of the full aperture is divided into multiple subapertures; then, the correlation between the two adjacent subapertures is used to perform correlation processing on the subaperture image, and then the DEM information is obtained. The chain correlation method proposed in this article takes advantage of the stronger correlation between adjacent subapertures, improves the accuracy of the algorithm, and effectively improves the overall efficiency of the algorithm. Finally, the effectiveness and reliability of the proposed method is verified by different measured data, and it is the first time our own collected Ku-band experimental data are used.

Multi-temporal UAV based repeat monitoring of rivers sensitive to flood

ABSTRACT Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Besides, the characteristics of the river crossing bridges may deteriorate due to flood induced scouring around bridge piles or due to flood loads. Thus, in this study high-resolution topography of the study region was acquired during two consecutive years by unmanned aerial vehicle (UAV) based surveys using Structure-from-Motion (SfM) processing. Following the extraction of digital elevation models (DEM), repeat data that were obtained at each UAV survey were compared using Geomorphic Change Detection (GCD) to calculate volumes of deposition and erosion via DEM of difference (DoD) algorithm. Thus, detailed high-resolution maps of the river channels can be rapidly and efficiently generated by low cost UAV based measurement methods in order for continuous tracking of stream channel morphology for the rivers sensitive to floods.

ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA)

Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.

The combination of attraction model and genetic algorithm to predict stream networks

The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds.

Filtering DSM extraction from Worldview-3 images to DTM using open source software

Today, the Indonesia one map policy has encouraged for producing maps up to scale 1/5.000 – 1/10.000. Therefore, all efforts to accelerate large-scale map production are still needed. One of the challenges is for producing DEM (Digital Elevation Model). DEM data extraction from high-resolution optical satellite imagery is an efficient choice for the production of large-area DEM. This study uses Worldview 3 stereo ready images for DSM (Digital Surface Model) Extraction. Although the DSM data have a high resolution 30cm x 30cm, unfortunately, the DEM extraction techniques from optical images produce DSM (Digital Surface Model) data instead of DTM. While topographic maps generally use DTM data instead of DSM. So, the goal of this study is to use an appropriate procedures method in Open-Source (OS) software towards the generation of Bare-Earth data by filtering DSM. The OS software is SAGA-GIS that it is can be used for any point cloud data. The quality of DTM generated by SAGA-GIS is evaluated by visual interpretation from Worldview 3 orthoimage itself. A good filtering result will remove the non-terrain point. Some independent check point used to evaluate the error in elevation. The error in elevation still less than 2 pixels. Final product is a high resolution DTM which has 30cm × 30cm grid spacing.

DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry

Digital elevation model (DEM) generation using multi-aspect synthetic aperture radar (SAR) imagery applying radargrammetry has become a hotspot. The traditional radargrammetric method is to solve the rigorous radar projection equations to obtain the three dimensional coordinates of targets. In this paper, we propose a new DEM generation method based on the offset between multi-aspect images formed on ground plane. The ground object will be projected to different positions from different viewing aspect angles if the height of object is not equal to the height of imaging plane. The linear relationship between the offset of imaging positions and height of the object is derived and scale factor is obtained finally. Height information can be retrieved from offset of imaging positions directly through the DEM extraction model presented in this paper. Thus the solution to nonlinear equations point by point can be avoided. Real C band airborne circular SAR images is used to verify the proposed approach. When extracted DEM applied in multi-aspect imaging process, superimposition of multi-aspect images will no longer be defocusing and can achieve finer observation of the scanned scene.

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

The isotropic organization of DEM structure and extraction of valley lines using hexagonal grid

AbstractThe automatic extraction of valley lines (VLs) from digital elevation models (DEMs) has had a long history in the GIS and hydrology fields. The quality of the extracted results relies on the geometrical shape, spatial tessellation, and placement of the grids in the DEM structure. The traditional DEM structure consists of square grids with an eight‐neighborhood relationship, where there is an inconsistent distance measurement between orthogonal neighborhoods and diagonal neighborhoods. The directional difference results in the extracted VLs by the D8 algorithm not guaranteeing isotropy characteristics. Alternatively, hexagonal grids have been proved to be advantageous over square grids due to their consistent connectivity, isotropy of local neighborhoods, higher symmetry, increased compactness, and more. Considering the merits above, this study develops an approach to VL extraction from DEMs based on hexagonal grids. First, the pre‐process phase contains the depression filling, flow direction calculation, and flow accumulation calculation based on the six‐neighborhood relationship. Then, the flow arcs are connected, followed by estimating the flow direction. Finally, the connected paths are organized into a tree structure. To explore the effectiveness of hexagonal grids, comparative experiments are implemented against traditional DEMs with square grids using three sample regions. By analyzing the results between these two grid structures via visual and quantitative comparison, we conclude that the hexagonal grid structure has an outstanding ability in maintaining the location accuracy and bending characteristics of extracted valley networks. That is to say, the DEM‐derived VLs based on hexagonal grids have better spatial agreement with mapped river systems and lower shape diversion under the same resolution representation. Therefore, the DEMs with hexagonal grids can extract finer valley networks with the same data volume relative to traditional DEM.

DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network

It is difficult to extract a digital elevation model (DEM) from an airborne laser scanning (ALS) point cloud in a forest area because of the irregular and uneven distribution of ground and vegetation points. Machine learning, especially deep learning methods, has shown powerful feature extraction in accomplishing point cloud classification. However, most of the existing deep learning frameworks, such as PointNet, dynamic graph convolutional neural network (DGCNN), and SparseConvNet, cannot consider the particularity of ALS point clouds. For large-scene laser point clouds, the current data preprocessing methods are mostly based on random sampling, which is not suitable for DEM extraction tasks. In this study, we propose a novel data sampling algorithm for the data preparation of patch-based training and classification named T-Sampling. T-Sampling uses the set of the lowest points in a certain area as basic points with other points added to supplement it, which can guarantee the integrity of the terrain in the sampling area. In the learning part, we propose a new convolution model based on terrain named Tin-EdgeConv that fully considers the spatial relationship between ground and non-ground points when constructing a directed graph. We design a new network based on Tin-EdgeConv to extract local features and use PointNet architecture to extract global context information. Finally, we combine this information effectively with a designed attention fusion module. These aspects are important in achieving high classification accuracy. We evaluate the proposed method by using large-scale data from forest areas. Results show that our method is more accurate than existing algorithms.

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

Photogrammetric reconstruction of changes in vertical river position using archival aerial photos: case study of the Czarny Dunajec River, Polish Carpathians

Rivers of the Polish Carpathians incised deeply during the twentieth century, but detailed information about the timing and amount of incision of their channels exists only for water-gauge cross sections. Applicability of photogrammetric extraction of digital elevation models (DEMs) from archival aerial photos for reconstructing changes in vertical river position was verified in the study of a 3-km reach of the Czarny Dunajec River. DEMs extracted from a few sets of archival aerial photos from the years 1964–1994 together with recent orthophotos and DEMs were used in the analysis. Measurements taken in river cross sections spaced at 100-m intervals indicated that on average the lowest point of the channel bed lowered between 1964 and 2009 by 1.74 ± 0.17 m, low-flow water surface by 1.57 ± 0.07 m, active river channel by 1.54 ± 0.12 m and the belt of river migration by 1.03 ± 0.15 m. However, the change in vertical river position during the years 1964–2009 varied greatly along the reach, with the elevation of low-flow water surface lowered by up to 3.61 ± 0.07 m in the upper part of the reach and increased by up to 1.34 ± 0.07 m in its lower part. Combining the information about changes in vertical river position and the width of river migration belt yielded data about the change in sediment volume in the reach, with an average annual loss of sediment amounting to 256 ± 37 m3 per 100-m channel segment. The study indicated that DEMs generated from archival aerial photos can be a useful tool in analysing recent vertical channel changes outside water-gauge stations.

Study of Digital Elevation Model (DEM) Extraction using Stereo Radargrammetry TerraSAR-X in Madiun Area – Elevation Accuracy Improvement

High resolution images data from Terrasar-X are used to extract digital elevation model (DEM) using stereo radargrammetry in the attempt to achieve better resolution of terrain surface in Indonesia. As sample in this study, stereo pairs images from TerraSAR-X StripMap mode (~3m resolution) on Madiun city is used with difference of incidence angle around ~18.88 degree to extract the elevation of the area. Furthermore, field observation on the selected area will be used on elevation accuracy assessment. The digital surface elevation (DSM) generated by stereo radargrammetry in this study shows us high resolution with spatial pixel spacing 5.57 meter and elevation accuracy around ~4 meter.

Extraction of digital elevation model from Chinese GF-3 C-band synthetic aperture radar data with a step-wise method focusing on facilitating phase unwrapping

GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM.

Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition

This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides.

Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

SRTM DEM-Aided Mapping Satellite-1 Image Geopositioning Without Ground Control Points

A Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)-aided geopositioning method is proposed to solve the problem of geopositioning without ground control points for Mapping Satellite-1 imagery. The method comprises coarse and accurate correction stages, and it compensates errors gradually. DEM extraction and DEM matching are important steps in both the stages, the objectives of which are to compensate the relative and absolute errors in an image, respectively. The SRTM DEM is integrated into all the processes to take full advantage of its consistent and high accuracy. Experimental results showed that this method could greatly improve geometry accuracy and obtain stable and highly accurate geopositioning for Mapping Satellite-1 images, regardless of the land area proportion (LAP) or the production mode. The planimetric and vertical accuracies were better than 8.1 and 5.2 m, respectively, which could satisfy the accuracy requirements of mapping at 1:50 000 scale. The computational efficiency depends on the LAP and target DEM resolution.

STUDY OF AUTOMATIC DEM (DIGITAL ELEVATION MODEL) PROCEDURE USING SATELLITE IMAGES OF PLEIADES (CASE STUDY OF BANDUNG - WEST JAVA)

The topographic maps can be derived from Digital Elevation Model (DEM), which can be obtained from satellite imagery data extraction. This result might be achieved by using a Very High-Resolution Satellites (VHRS) Imagery, such as Pleiades Satellite. The main objective of this research is to review the procedures of Automatic DEM Extraction in order to obtain minimum error. The systematic implementations of this research are modeling the sensor, generating automatic tie point and performing automatic DEM extraction. The results of DEM extraction can be either point cloud data or mesh data which are verified by comparing the reference data. This is lead to acquire pairing combination of Pleiades imagery data which has the smallest error value among all of them. The comparison showed that the tri stereo pairing combination of the DEM extraction model has a lower accuracy than the backward – nadir pairing combination.

Impacts of DEM resolution and area threshold value uncertainty on the drainage network derived using SWAT

Many hydrological algorithms have been developed to automatically extract drainage networks from DEM, and the D8 algorithm is widely used worldwide to delineate drainage networks and catchments. The simulation accuracy of the SWAT model depends on characteristics of the watershed, and previous studies of DEM resolution and its impacts on drainage network extraction have not generally considered the effects of resolution and threshold value on uncertainty. In order to assess the influence of different DEM resolutions and drainage threshold values on drainage network extraction using the SWAT model, 10 basic watershed regions in China were chosen as case studies to analyse the relationship between extracted watershed parameters and the threshold value. SRTM DEM data at 3 different resolutions were used in this study, and regression analysis for DEM resolution, threshold value and extraction effects was done. The results show that DEM resolution influences the selected flow accumulation threshold value; the suitable flow accumulation threshold value increases as the DEM resolution increases, and shows greater variability for basins with lower drainage densities. The link between drainage area threshold value and stream network extraction results was also examined, and showed a variation trend of power function y = ax b between the sub-basin counts and threshold value, i.e., the maximum reach length increases while the threshold value increases, and the minimum reach length shows no relation with the threshold value. The stream network extraction resulting from a 250 m DEM resolution and a 50 000 ha threshold value was similar to the real stream network. The drainage network density and the threshold value also shows a trend of power function y = ax b ; the value of b is usually 0.5. Keywords: SWAT, digital elevation model (DEM), watershed delineation, threshold value

The Surface Extraction from TIN based Search-space Minimization (SETSM) algorithm

Digital Elevation Models (DEMs) provide critical information for a wide range of scientific, navigational and engineering activities. Submeter resolution, stereoscopic satellite imagery with high geometric and radiometric quality, and wide spatial coverage are becoming increasingly accessible for generating stereo-photogrammetric DEMs. However, low contrast and repeatedly-textured surfaces, such as snow and glacial ice at high latitudes, and mountainous terrains challenge existing stereo-photogrammetric DEM generation techniques, particularly without a-priori information such as existing seed DEMs or the manual setting of terrain-specific parameters. To utilize these data for fully-automatic DEM extraction at a large scale, we developed the Surface Extraction from TIN-based Search-space Minimization (SETSM) algorithm. SETSM is fully automatic (i.e. no search parameter settings are needed) and uses only the sensor model Rational Polynomial Coefficients (RPCs). SETSM adopts a hierarchical, combined image- and object-space matching strategy utilizing weighted normalized cross-correlation with both original distorted and geometrically corrected images for overcoming ambiguities caused by foreshortening and occlusions. In addition, SETSM optimally minimizes search-spaces to extract optimal matches over problematic terrains by iteratively updating object surfaces within a Triangulated Irregular Network, and utilizes a geometric-constrained blunder and outlier detection in object space. We prove the ability of SETSM to mitigate typical stereo-photogrammetric matching problems over a range of challenging terrains. SETSM is the primary DEM generation software for the US National Science Foundation’s ArcticDEM project.