scholarly journals An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery

2016 ◽  
Vol 116 ◽  
pp. 101-117 ◽  
Author(s):  
David E. Shean ◽  
Oleg Alexandrov ◽  
Zachary M. Moratto ◽  
Benjamin E. Smith ◽  
Ian R. Joughin ◽  
...  
Author(s):  
T. Kramm ◽  
D. Hoffmeister

<p><strong>Abstract.</strong> The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000&amp;thinsp;km<sup>2</sup>. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30&amp;thinsp;m and 90&amp;thinsp;m, Advanced Land Observing Satellite (ALOS) World 3D 30&amp;thinsp;m and TanDEM-X WorldDEM&amp;trade; &amp;ndash; 12&amp;thinsp;m and 90&amp;thinsp;m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100&amp;ndash;400&amp;thinsp;km<sup>2</sup> for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12&amp;thinsp;m (RMSE: 2.3&amp;thinsp;m, NMAD: 0.8&amp;thinsp;m). The lowest accuracies were detected for the 30&amp;thinsp;m ASTER GDEM v3 (RMSE: 8.9&amp;thinsp;m, NMAD: 7.1&amp;thinsp;m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.</p>


2019 ◽  
Vol 8 (2) ◽  
pp. 293-313 ◽  
Author(s):  
Adam J. Hepburn ◽  
Tom Holt ◽  
Bryn Hubbard ◽  
Felix Ng

Abstract. The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to low-resolution global datasets, such models enable the study of landforms <10 km in size, which is the primary scale at which geomorphic processes have been active on Mars over the last 10–20 Myr . Stereogrammetry is a means of producing digital elevation models from stereo pairs of images. The HiRISE camera on board the Mars Reconnaissance Orbiter has captured >3000 stereo pairs at 0.25 m pixel−1 resolution, enabling the creation of high-resolution digital elevation models (1–2 m pixel−1). Hitherto, only ∼500 of these pairs have been processed and made publicly available. Existing pipelines for the production of digital elevation models from stereo pairs, however, are built upon commercial software, rely upon sparsely available intermediate data, or are reliant on proprietary algorithms. In this paper, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open-source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is designed for simple application by researchers interested in the use of high-resolution digital elevation models. Implemented here on a research computing cluster, this pipeline can also be used on consumer-grade UNIX computers. We produce and evaluate four digital elevation models using the pipeline presented here. Each are globally well registered, with accuracy similar to those of digital elevation models produced elsewhere.


2015 ◽  
Vol 6 (12) ◽  
pp. 1373-1383 ◽  
Author(s):  
Kevin Leempoel ◽  
Christian Parisod ◽  
Céline Geiser ◽  
Lucas Daprà ◽  
Pascal Vittoz ◽  
...  

2020 ◽  
Author(s):  
Robert McNabb ◽  
Luc Girod ◽  
Christopher Nuth ◽  
Andreas Kääb

&lt;p&gt;First launched in 1971, the KH-9 &amp;#8220;Hexagon&amp;#8221; reconnaissance satellites were operational until 1986. In addition to the high-resolution main cameras, the satellites had a secondary camera system, the mapping camera, which acquired images at approximately 6-10m ground resolution. These images, declassified in 2002, provide an unparalleled ability to extend records of elevation change over areas of the world where older data, typically from aerial photogrammetry, are missing, unavailable, or unreliable, including High Mountain Asia and the Arctic. These images are not, however, free from challenges. Storage and film processing have introduced warping into the images, and the large film format means that images are scanned in halves which must be precisely re-aligned for photogrammetric processing.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Building on previous efforts, we have developed an open-source toolset, based in python, that performs several of the steps necessary for processing digital elevation models (DEMs) from the raw imagery within MicMac. These include precise re-alignment based on dense keypoint detection, automated detection of the reseau field to aid in un-warping of the images, color balancing to increase contrast in low-contrast areas, and automated detection of ground control points using modern orthorectified satellite images such as Sentinel-2 and Landsat 8, and high-resolution digital elevation models such as ArcticDEM. Each of these tools interface with the MicMac photogrammetry software package that performs each of the steps necessary for DEM extraction.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We have tested this toolset on scenes from Alaska, Iceland, and Norway. Comparison to external elevation datasets such as NASA&amp;#8217;s Ice, Cloud and Elevation Satellite (ICESat), ArcticDEM, and national elevation products yields agreement of better than 10 m root mean square error over stable terrain, even in mountainous areas. In particular, we obtain satisfactory results in remote areas where precise ground control measurements are difficult to obtain. This toolset provides the ability to easily extend records of precise elevation change in areas where very little historic data exist. In addition, the GCP matching routine can be used to process other air photo datasets, providing a useful tool for processing older photo archives.&lt;/p&gt;


2019 ◽  
Author(s):  
Adam J. Hepburn ◽  
Tom Holt ◽  
Bryn Hubbard ◽  
Felix Ng

Abstract. The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to the globally-available but low-resolution datasets, such models enable the study of landforms  3000 stereo pairs at 25 cm/pixel resolution, enabling the creation of high-resolution digital elevation models (1–2 m/pixel). However, only ~ 500 of these pairs have been processed and made publicly available to date. Existing pipelines for the production of digital elevation models from stereo-pairs, however, are built upon commercial software, rely upon sparsely-available intermediate data, or are reliant on proprietary algorithms. Here, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is implemented here on a research computing cluster, but can also be used on consumer-grade UNIX computers. The four output digital elevation models produced using the pipeline presented here are globally well-registered, with accuracy similar to those of multiple digital elevation models produced elsewhere.


2020 ◽  
Vol 38 (11A) ◽  
pp. 1580-1592
Author(s):  
Imzahim A. Alwan ◽  
Zina W. Samueel ◽  
Qassim K. Abdullah

Digital Elevation Models (DEM) are now being used in several geospatial applications. DEMs play an important role in the preliminary surveys for constructing dams and reservoirs, highways, canals, and projects in which earth work is essential. In many remote sensing applications, DEMs have become a significant tool for InSAR (Interferometric Synthetic Aperture Radar) processing, ground cover classification and images ortho-rectification. In this study, the accuracy of DEMs obtained from ALOS V1.1, ASTER V2, SRTM V3 and other obtained from a pair of Pleiades high-resolution (PHR) 1B satellites in a study area were evaluated after comparing them with high accuracy GNSS/RTK checkpoints. The SRTM3, ALOS V1.1, ASTER V2 DEM revealed a Root Mean Square Error (RMSE) of 2.234m, 0.838m, and 15.116m respectively; while the DEM which is produced from a 0.5m resolution of Pleiades 0.5m shows an RMSE of 0.642m. The correct bias Linear transformation algorithm was used and the RMSE results were: SRTM V3 (1.319m), ALOS V1.1 (0.830m), ASTER V2 (3.815m), and PHR (0.433m). The results showed that the ALOS V1.1 model is the most accurate of the open source models followed by the SRTM V3 model and then followed by ASTER V2. The results obtained from a pair by Pleiades high-resolution (PHR) 1B satellites show a higher accuracy than the results obtained from the open source models.


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