Skip to content
naulon
Not checkedNot checked for notices yet.

A New Digital Terrain Model of the Huygens Landing Site on Saturn's Largest Moon, Titan

C. Daudon, A. Lucas, S. Rodriguez and 9 more

Earth and Space Science · 2020

doi:10.1029/2020ea001127 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

Abstract River valleys have been observed on Titan at all latitudes by the Cassini‐Huygens mission. Just like water on Earth, liquid methane carves into the substrate to form a complex network of rivers, particularly stunning in the images acquired near the equator by the Huygens probe. To better understand the processes at work that form these landscapes, one needs an accurate digital terrain model (DTM) of this region. The first and to date the only existing DTM of the Huygens landing site was produced by the U.S. Geological Survey (USGS) from high‐resolution images acquired by the DISR (Descent Imager/Spectral Radiometer) cameras on board the Huygens probe and using the SOCET SET photogrammetric software. However, this DTM displays inconsistencies, primarily due to nonoptimal viewing geometries and to the poor quality of the original data, unsuitable for photogrammetric reconstruction. We investigate a new approach, benefiting from a recent reprocessing of the DISR images correcting both the radiometric and geometric distortions. For the DTM reconstruction, we use MicMac, a photogrammetry software based on automatic open‐source shape‐from‐motion algorithms. To overcome challenges such as data quality and image complexity (unusual geometric configuration), we developed a specific pipeline that we detailed and documented in this article. In particular, we take advantage of geomorphic considerations to assess ambiguity on the internal calibration and the global orientation of the stereo model. Besides the novelty in this approach, the resulting DTM obtained offers the best spatial sampling of Titan's surface available and a significant improvement over the previous results.

Abstract licence: CC BY-NC-ND

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.