Engineering topics
Schorghofer, Norbert
Publications and source records attributed to Schorghofer, Norbert.
The Putative Cerean Exosphere
The ice-rich crust of dwarf planet 1 Ceres is the source of a tenuous water exosphere, and the behavior of thisputative exosphere is investigated with model calculations. Outgassing water molecules seasonally condensearound the winter pole in an optically thin layer.
The Permanently Shadowed Regions of Dwarf Planet Ceres
Ceres has only a small spin axis tilt (4 deg), and craters near its rotational poles can experience permanent shadow and trap volatiles, as is the case on Mercury and on Earth's Moon. Topography derived from stereo imaging by the Dawn spacecraft is used to calculate direct solar irradiance that defines the extent of the permanently shadowed regions (PSRs). In the northern polar region, PSRs cover approximately 1800 sq km or 0.13% of the hemisphere, and most of the PSRs are cold enough to trap water ice over geological time periods. Based on modeling of the water exosphere, water molecules seasonally reside around the winter pole and ultimately an estimated 0.14% of molecules get trapped. Even for the lowest estimates of the amount of available water, this predicts accumulation rates in excess of loss rates, and hence, there should be fresh ice deposits in the cold traps.
Landmark Detection in Orbital Images Using Salience Histograms
NASA's planetary missions have collected, and continue to collect, massive volumes of orbital imagery. The volume is such that it is difficult to manually review all of the data and determine its significance. As a result, images are indexed and searchable by location and date but generally not by their content. A new automated method analyzes images and identifies "landmarks," or visually salient features such as gullies, craters, dust devil tracks, and the like. This technique uses a statistical measure of salience derived from information theory, so it is not associated with any specific landmark type. It identifies regions that are unusual or that stand out from their surroundings, so the resulting landmarks are context-sensitive areas that can be used to recognize the same area when it is encountered again. A machine learning classifier is used to identify the type of each discovered landmark. Using a specified window size, an intensity histogram is computed for each such window within the larger image (sliding the window across the image). Next, a salience map is computed that specifies, for each pixel, the salience of the window centered at that pixel. The salience map is thresholded to identify landmark contours (polygons) using the upper quartile of salience values. Descriptive attributes are extracted for each landmark polygon: size, perimeter, mean intensity, standard deviation of intensity, and shape features derived from an ellipse fit.
Valley Network Morphology and Topographic Gradients on Mars
Data returned from the Mars Orbiter Laser Altimeter allows construction of a high precision digital elevation model. Quantitative investigations into the geomorphic properties of drainage features, similar to ones carried out on Earth, are now possible Additional information is contained in the original extended abstract.