The objective of this work was to autonomously detect manually verified features (plumes) in images under onboard conditions. Success enables these methods to be applied to future outer solar system missions and facilitates onboard autonomous detection of transient events and features regardless of viewing and illumination effects, electronic interference, and physical image arti facts. Autonomous detection allows the maximization of the spacecrafts memory capacity and downlink bandwidth byprioritizing data of utmost scientific significance.
What are plumes? The indication of active transient volcanic eruptive events on multi-
ple planetary bodies (Figure 1). These include large scale explosive and effusive eruptions on Jupiters satellite, Io, geyser-like ejections on Saturns satellite, Enceladus, and
Neptunes satellite, Triton, and outgassing of comet nuclei. These phenomena provide key constraints for subsurface processes, interior dynamics, surface-interior interactions,
and models of planetary composition.
The methods: After experimental evaluation of the efficacy of various autonomous supervised classification techniques. We found that Scale Invariant Feature Transform(SIFT) + K Nearest Neighbour(KNN) gave the best results. (Figure 2). The statistics of the detection of different image sets are in Table 1.
Fig2. Results of SIFT+KNN. Detected plumes are denoted with blue arrows.Some plumes are enlarged and enhanced for clarity
Table 1. Statistics of plume detection for different image sets