Magellan mission summary

Abstract

Magellan started mapping the planet Venus on September 15, 1990, and after one cycle (one Venus day or 243 Earth days) had mapped 84% of the planet's surface. This returned an image data volume greater than all past planetary missions combined. Spacecraft problems were experienced in flight. Changes in operational procedures and reprogramming of onboard computers minimized the amount of mapping data lost. Magellan data processing is the largest planetary image‐processing challenge to date. Compilation of global maps of tectonic and volcanic features, as well as impact craters and related phenomena and surface processes related to wind, weathering, and mass wasting, has begun. The Magellan project is now in an extended mission phase, with plans for additional cycles out to 1995. The Magellan project will fill in mapping gaps, obtain a global gravity data set between mid‐September 1992 and May 1993, acquire images at different view angles, and look for changes on the surface from one cycle to another caused by surface activity such as volcanism, faulting, or wind activity.

Comparison of flat-topped stellate seamounts on Earth's seafloor with stellate domes on Venus using side-scan sonar and Magellan synthetic aperture radar

“…Magellan's radar system operated at a 12.6 cm wavelength and collected synthetic aperture radar SAR images, radar altimetry and radiometry data [22]. The spatial resolution of the SAR is comparable to GLORIA sonar resolution ( [24], prohibiting any quantitative comparisons between the two data sets.…”

The Basic Principles of Creation of Habitable Planets around Stars in the Milky Way Galaxy

“…The Venusian Topography Map available on line at [59]. The Venusian topography was made by using synthetic aperture radar (SAR) during Magellan satellite mission (see [60] [61]). The details about Venusian topography may be found in [62] [63] [64].…”

HNNP - A Hybrid Neural Network Plait for Improving Image Classification with Additional Side Information

“…IV-B) comes from a set of synthetic aperture radar (SAR) images of the surface of Venus, available at the UCI Machine Learning Repository [1]. The data were collected by the Magellan spacecraft from 1990-1994 to obtain a mapping of the surface of Venus by means of SAR technology ( [15], [16]). The classification task in this data is the identification of volcanoes.…”

“…Those data sets come for instance from problems in biomedicine, like cutaneous melanoma identification ( [13]), brain tumor segmentation by magnetic resonance imaging ( [9]) or analyzing microscopy images ( [14]). Further examples are problems in the field of radar like detecting reflections of buried objects in ground penetrating radar images (see [6], [7], [8]) or recognizing volcanoes or craters on synthetic aperture radar images of the surface of planets like Mars or Venus ( [15], [16], [19], [3]). The reason for small data sets in this fields is the c 2013 IEEE (Published in proceedings of ICTAI 2013) often very expensive and time consuming recording of the images as well as the need of manual labeling.…”