Helen B. Mortensen

Calibration and performance of the Galileo solid-state imaging system in Jupiter orbit

The solid-state imaging subsystem (SSI) on the National Aeronautics and Space Administration’s (NASA’s) Galileo Jupiter orbiter spacecraft has successfully completed its 2-yr primary mission exploring the Jovian system. The SSI has remained in remarkably stable calibration during the 8-yr flight, and the quality of the returned images is exceptional. Absolute spectral radiometric calibration has been determined to 4 to 6% across its eight spectral filters. Software and calibration files are available to enable radiometric, geometric, modulation transfer function (MTF), and scattered light image calibration. The charge-coupled device (CCD) detector endured the harsh radiation environment at Jupiter without significant damage and exhibited transient image noise effects at about the expected levels. A lossy integer cosine transform (ICT) data compressor proved essential to achieving the SSI science objectives given the low data transmission rate available from Jupiter due to a communication antenna failure. The ICT compressor does introduce certain artifacts in the images that must be controlled to acceptable levels by judicious choice of compression control parameter settings. The SSI team’s expertise in using the compressor improved throughout the orbital operations phase and, coupled with a strategy using multiple playback passes of the spacecraft tape recorder, resulted in the successful return of 1645 unique images of Jupiter and its satellites.

Using artificial intelligence planning to automate science image data analysis

In recent times, improvements in imaging technology have made available an incredible array of information in image format. While powerful and sophisticated image processing software tools are available to prepare and analyze the data, these tools are complex and cumbersome, requiring significant expertise to properly operate. Thus, in order to extract e.g., mine or analyze useful information from the data, a user in our case a scientist often must possess both significant science and image processing expertise.This article describes the use of artificial intelligence AI planning techniques to represent scientific, image processing and software tool knowledge to automate knowledge discovery and data mining e.g., science data analysis of large image databases. In particular, we describe two fielded systems. The Multimission VICAR Planner MVP which has been deployed for since 1995 and is currently supporting science product generation for the Galileo mission. MVP has reduced time to fill certain classes of requests from 4 h to 15 min. The Automated SAR Image Processing system ASIP was deployed at the Department of Geology at Arizona State University in 1997 to support aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation by ten-fold.