Recovery of elevation from estimated gradient fields constrained by digital elevation maps of lower lateral resolution |
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| Institution: | 1. Department of Physics and Astronomy, Uppsala University, Box 516, SE-75120 Uppsala, Sweden;2. PAN Space Research Center, Bartycka 18A, PL-00716 Warsaw, Poland;3. Space Science Institute, 4750 Walnut St. Suite 205, Boulder, CO 80301, USA;4. Aix Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille) UMR 7326, 13388 Marseille, France;5. Instituto de Astrofísica de Andalucía-CSIC, Aptd. 3004, 18080 Granada, Spain;6. INAF–IASF, Via del Fosso del Cavaliere 100, 00133 Roma, Italy;7. Earth and Planetary Sciences, University of Tennessee, 306 Earth and Planetary Sciences Building, Knoxville, TN 37996-1410, USA;8. Deutsches Zentrum für Luft- und Raumfahrt e.V. in der Helmholtz-Gemeinschaft, Berlin-Adlershof, Rutherfordstraße 2, 12489 Berlin, Germany;9. Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse, 85748 Garching, Germany;1. Technische Universität Berlin, Institute of Geodesy and Geoinformation Science, 10623 Berlin, Germany;2. German Aerospace Center, Institute of Planetary Research, 12489 Berlin, Germany;3. Extraterrestrial Laboratory, Moscow State University for Geodesy and Cartography, RU-105064 Moscow, Russia;4. NASA Goddard Space Flight Center, Code 698, Greenbelt, MD 20771, USA;5. Arizona State University, School of Earth and Space Exploration, Tempe, AZ 85287, USA |
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| Abstract: | Depth measurement techniques like stereo analysis and laser range scanning often yield a lateral resolution below image resolution. In contrast, shading-based methods estimate the elevation model at image resolution. We present a computationally efficient approach to recover a surface of high vertical and high lateral resolution from a noisy gradient field and independently measured elevation data of lower lateral resolution, relying on a minimization of the mean squared difference between the low-pass component of the surface obtained based on shading information and that of the independently measured elevation data. The presented method is compared to a reference approach that minimizes a weighted sum comprised of the mean squared difference between the low-pass components of the estimated gradient field and the optimized model, respectively. The presented algorithm is applied using lunar orbital image data and stereo elevation data and is evaluated regarding orbital laser altimeter measurements of high vertical accuracy. |
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| Keywords: | DEM/DTM generation Surface reconstruction Image analysis |
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