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Roden, J.

Publications and source records attributed to Roden, J..

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Airborne Radar Study of Mars Analogs in the Southwestern United States

The search for surface and near-surface liquid water on Mars is a central part of current and planned future exploration, which include radar sounders on Mars Express and MRO and proposed synthetic aperture radar (SAR) imagers. In order to penetrate sand and dust cover, these systems are proposed for longer wavelengths (e.g, from [2]: 24 cm / L-band and 74 cm / P-band) than those considered optimal for the detection of soil moisture (6 cm / C-band). However, there has been some success in detecting soil moisture at longer wavelengths. Given the size and mass constraints for Mars missions, the optimization of radar instrument parameters for meeting science objectives, such as searching for liquid water, is essential. In this on-going study, we are using repeat coverage of Mars analog sites with multifrequency (C, L and P band) airborne radar and ground truth soil sample data to assess the detectability of soil moisture.

Greeley, R.↗
NASA NTRS2002

The Techsat-21 autonomous space science agent

In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent.

autonomy software agents onboard decision-making↗
NASA NTRS1992

SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.

Weir, N.↗