A number of deep space missions have imaged plumes at Io, Enceladus, and other smaller bodies. These phenomena provide valuable information regarding these bodies. To date, this imagery has been captured fortuitously. The ability to utilize onboard processing to conduct campaigns capturing large numbers of images and to detect when a plume event is occurring would open up new mission paradigms. Extended temporal campaigns could provide comprehensive detail on these events’ frequency and character.
This software enables detection of plumes and obtrusions from irregularly shaped bodies. It finds convex hull from edge detection. It is very computationally efficient, and works with irregularly shaped bodies.
Onboard plume detection will enable much more efficient monitoring of outbursts and other dynamic phenomena. These techniques can enable a new class of missions to conduct long-term continuous monitoring of moons, planets, and comets.
This work was done by David R. Thompson, Steve A. Chien, Daniel Q. Tran, and Rebecca Castano of Caltech; and Ronald Greeley and Melissa Bunte of Arizona State University for NASA’s Jet Propulsion Laboratory. NPO-48432
This Brief includes a Technical Support Package (TSP).
Convex Hull-Based Plume and Anomaly Detection
(reference NPO-48432) is currently available for download from the TSP library.
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Overview
The document titled "Automated Onboard Detection of Cometary Outbursts, Plumes, and Other Dynamic Spatial Science Phenomena" outlines a project conducted by researchers at NASA's Jet Propulsion Laboratory (JPL) aimed at enhancing the detection of plumes and other dynamic phenomena on celestial bodies such as moons and comets. The primary objective is to develop onboard processing capabilities that allow spacecraft to autonomously capture and analyze images, thereby improving the efficiency and frequency of monitoring these events.
Historically, imaging of plumes on bodies like Io and Enceladus has been conducted fortuitously, limiting the understanding of their frequency and characteristics. The proposed approach involves a systematic method for detecting these phenomena using advanced image processing techniques. The detection process includes two main steps: edge detection using the Canny edge detection algorithm and horizon fitting through a RANSAC (Random Sample Consensus) method. This involves sampling random subsets of edge points to fit an ellipse to the horizon, allowing for the identification of plume events.
The significance of this research lies in its potential to revolutionize deep space missions. By enabling onboard plume detection, missions can conduct long-term continuous monitoring of celestial bodies, leading to a better understanding of their geological and atmospheric processes. This capability could facilitate new mission paradigms, allowing for more comprehensive data collection and analysis over extended periods.
The document also references previous work in the field, highlighting the evolution of techniques for automatic detection of planetary phenomena. It emphasizes the importance of these advancements in the context of space exploration and the scientific insights they can provide.
In summary, the document presents a forward-looking approach to automated detection of dynamic spatial phenomena in space, showcasing the innovative methodologies being developed at JPL. By integrating advanced image processing techniques into spacecraft operations, this research aims to enhance our understanding of celestial bodies and their behaviors, ultimately contributing to the broader goals of planetary science and exploration.
