Recovery of elevation from estimated gradient fields constrained by digital elevation maps of lower lateral resolution

Grumpe, A.; Wöhler, Christian

doi:10.1016/j.isprsjprs.2014.04.011

Cited by 22 publications

(41 citation statements)

References 22 publications

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“…The absolute depth error

E_{depth} = \frac{1}{2} \int_{x} \int_{y} {(f_{σ} (z) - f_{σ} (z_{DTM}))}^{2} italicdx italicdy

is introduced by Grumpe and Wöhler (2014) and further restricts the surface model z to follow the large scale trend of z DTM .…”

Section: Methodsmentioning

confidence: 99%

“…The ability to characterize the nature and distribution of small geological features such as these has substantially improved in the last decade, due to the acquisition of global high‐resolution imagery (e.g., Kato et al, 2010; Robinson et al, 2010), in many cases offering a wide range of viewing geometries and illumination conditions. High‐resolution altimetry has also been derived from direct measurements (e.g., laser altimetry; Barker et al, 2016 and Smith et al, 2010) or derived from stereophotogrammetry (e.g., Scholten et al, 2012) and shape from shading (Grumpe et al, 2014, 2018; Grumpe & Wöhler, 2014). This allows RMDS morphology to be reconstructed and their volumes to be derived.…”

Section: Introductionmentioning

confidence: 99%

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Ring‐Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin

et al. 2020

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Ring‐moat dome structures (RMDSs) are positive morphologic features found clustered across many mare regions on the Moon, of which only a few isolated examples have been previously reported. Our continuing survey has expanded the known locations of the RMDSs from ~2,600 to over 8,000, indicating that RMDSs are more common geological features than previously thought. This work presents a detailed geomorphological analysis of 532 RMDSs identified in several mare basins. The combination of detailed elemental mapping, morphological and morphometric analyses, spatial distribution relationships with other geologic structures, and comparison with terrestrial analogs lead us to conclude that (1) RMDSs represent low circular mounds with diameters of a few hundred meters (average about 200 m) and a mean height of 3.5 m. The mounds are surrounded by moats ranging from tens to over 100 m in width and up to several meters in depth; (2) there is a wide variation of titanium abundances, although RMDSs are more commonly found in mare regions of moderate‐to‐high titanium content (>3 wt% TiO2); (3) RMDSs are found to occur on or around fractures, graben, and volcanic edifices (small shields and cones); (4) a spatial association between RMDSs and Irregular Mare Patches (see Braden et al., 2014, https://doi.org/10.1038/ngeo2252) is observed, suggesting that both may form from related lava flows; (5) comparisons between RMDSs and lava inflationary structures on Earth support an inflation‐related extrusive nature and a genetic relationship with host lava flow processes; and (6) RMDS embayment relationships with craters of different degradation ages superposed on the host mare, and regolith development models, produces conflicting age relationships and divide theories of RMDS origin into two categories, (1) synchronous with the emplacement and cooling of the host lava flows ~3–4 Ga and (2) emplaced substantially after the host mare lava unit, in the period ~0–3 Ga. We outline the evidence supporting this age conundrum and implications for the different theories of origin and describe future research avenues to help resolve these outstanding questions.

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“…The absolute depth error

E_{depth} = \frac{1}{2} \int_{x} \int_{y} {(f_{σ} (z) - f_{σ} (z_{DTM}))}^{2} italicdx italicdy

is introduced by Grumpe and Wöhler (2014) and further restricts the surface model z to follow the large scale trend of z DTM .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ring‐Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin

et al. 2020

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“…It is thus not relevant for this study and is not discussed further. For more detailed information about the M 3 data processing framework, see previous studies ( 5 , 40 , 49 , 50 ).…”

Section: Methodsmentioning

confidence: 99%

Time-of-day–dependent global distribution of lunar surficial water/hydroxyl

et al. 2017

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“…To solve the underdetermined problem, regularization terms are needed, such as the smoothness constraint, the integrability constraint, the gradient constraint, the unit normal constraint and so on [6]. For previous studies in photoclinometry, some methods [21,28] get p and q first, then acquire z by integrating p and q, while other methods [8,20,29] include z in the optimization scheme and solve for z directly. We prefer the second choice since it avoids the accumulation of errors and is more concise in the formulation.…”

Section: A Mathematical and Physical Basis Of The Photoclinometric Modelmentioning

confidence: 99%

“…For regularization-level methods [17][18][19][20][21], the low-resolution DEM produced by photogrammetry is integrated in a regularization term in the photoclinometry model. In addition the low-resolution DEM constitutes an a priori solution of the latter.…”

Section: Introductionmentioning

confidence: 99%

Small-Scale Topographical Characterization of the Martian Surface With In-Orbit Imagery

Douté

Cheng

2020

IEEE Trans. Geosci. Remote Sensing

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We present a new method applying photoclinometry on photogrammetric coarse products in order to produce high quality, large Digital Elevation Models (DEMs) from images of Mars acquired by the Context Camera (CTX) and the High Resolution Imaging Science Experiment (HiRISE) of the Mars Reconnaissance Orbiter. The method integrates an intensity model of the image based on a novel radiative transfer scheme and a realistic bidirectional reflectance distribution function (BRDF) model of the surface. Two carefully crafted regularization terms are also introduced ensuring the smoothness of the solution and the consistency with the photogrammetric information at large scales.The regularized inversion of the model based on an efficient numerical optimization scheme allows the method to recover details missing or degraded in the photogrammetrically generated DEMs. In addition for processing challenging scenes a coarse-to-fine strategy is put forward. The refined DEMs are validated and carefully characterized by multi-scale analysis. The method insures a spatial resolution that is comparable to that of the imagery and allows measuring heights with a relative precision down to 15 centimeters with HiRISE imagery.

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Recovery of elevation from estimated gradient fields constrained by digital elevation maps of lower lateral resolution

Cited by 22 publications

References 22 publications

Ring‐Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin

Ring‐Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin

Time-of-day–dependent global distribution of lunar surficial water/hydroxyl

Small-Scale Topographical Characterization of the Martian Surface With In-Orbit Imagery

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