Algorithms for Autonomous Plume Detection on Outer Planet Satellites

Details Algorithms for Autonomous Plume Detection on Outer Planet Satellites Algorithms for Autonomous Plume Detection on Outer Planet Satellites

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufm.p11a1572l&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #P11A-1572

Computer Science

Performance

[6219] Planetary Sciences: Solar System Objects / Io, [6280] Planetary Sciences: Solar System Objects / Saturnian Satellites, [6297] Planetary Sciences: Solar System Objects / Instruments And Techniques, [6299] Planetary Sciences: Solar System Objects / General Or Miscellaneous

Scientific paper

We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to increase effectiveness. We have demonstrated an ability to detect transient events above the planetary limb and on the surface and to distinguish feature classes in spacecraft images.

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