A computer program designed for execution aboard an exploratory spacecraft analyzes scientific data (especially image data) in order to (1) enable the reservation of limited communication resources for transmission of data likely to be of significant scientific value and (2) enable automated, rapid response to take advantage of fleeting, unanticipated opportunities for important scientific observations. The program can also be executed on Earth to analyze data acquired in prior spacecraft missions. At its present state of development, the program implements change detection and discovery algorithms that recognize scientifically interesting features in images of terrain of remote planets, moons, asteroids, and the like. These algorithms utilize examples of previously identified targets to generate efficient mathematical models for identifying new targets of the same type across a continuous range of scales. In tests thus far, the program recognized 80 percent of craters, with a false-alarm rate of 12 percent, in Lunar images larger than four pixels acquired by the Clementine spacecraft. The program has also been shown to be capable of discovering volcanoes on Venus, sand dunes on Mars, and ice geysers (cryovolcanoes) on Neptune’s moon Triton.
This program was written by Ashley Davies, Eric Mjolsness, Joseph Roden, Michael Burl, Rebecca Castano, Robert Sherwood, Steve Chien, and Timothy Stough of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393- 3425. Refer to NPO-30442.
This Brief includes a Technical Support Package (TSP).
Software for Ananlyzing Scientific Data Abroad a Spacecraft
(reference NPO-30442) is currently available for download from the TSP library.
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Overview
The document outlines a software program developed for NASA's Jet Propulsion Laboratory (JPL) aimed at analyzing scientific data aboard exploratory spacecraft. This program is designed to optimize the use of limited communication resources by prioritizing the transmission of data that holds significant scientific value. Additionally, it enables automated and rapid responses to unexpected scientific opportunities, enhancing the efficiency of data collection during missions.
The software employs advanced change-detection and discovery algorithms that can recognize scientifically interesting features in images of various celestial bodies, including planets, moons, and asteroids. These algorithms utilize previously identified targets to create efficient mathematical models, allowing for the identification of new targets across a continuous range of scales. In tests conducted with images from the Clementine spacecraft, the program demonstrated an 80% success rate in recognizing craters larger than four pixels, with a false-alarm rate of 12%. Furthermore, it has shown capabilities in discovering geological features such as volcanoes on Venus, sand dunes on Mars, and cryovolcanoes on Neptune’s moon Triton.
The development of this software addresses several challenges faced in space exploration, including limited downlink bandwidth, the need for quick responses to dynamic scientific events, and the labor-intensive nature of ground science analysis. By automating the detection and classification of geological features, the program significantly enhances the scientific return from missions.
The document also includes information about the inventors of the software: Ashley G. Davies, Eric D. Mjolsness, Joseph C. Roden, Michael C. Burl, Rebecca Castano, Robert L. Sherwood, Steve A. Chien, and Timothy M. Stough. It notes that the work was carried out under a contract with NASA and emphasizes that the software is available for commercial licensing through the California Institute of Technology.
In summary, this document presents a significant advancement in the field of space exploration, showcasing a software program that not only improves data analysis aboard spacecraft but also enhances the ability to respond to scientific opportunities in real-time, ultimately contributing to a greater understanding of our solar system.
