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Description
Hyperspectral data received from missions like Cassini-Huygens provide detailed insights about the surface composition of bodies in the outer solar system. However, existing spectral classification methods either require heavy computation, extensive observational data, or manual fine-tuning of features. This study proposes the use of classification accuracy as a similarity metric, using a Support Vector Machine (SVM) to distinguish between pairs of spectral data, with the ability of the model to distinguish the spectra (accuracy) being inversely proportional to the similarity of samples. Data from three compositionally distinct moons of Saturn (Titan, Rhea, and Enceladus) was used in pairs with binary classification and 5-fold cross-validation for robust accuracy estimates. The results showed a 96.9% accuracy for the Titan-Enceladus pair, which is explained by stark differences in albedo and composition, an 81.6% accuracy for the Titan-Rhea pair, explained by compositional differences but similarity in tholin presence on their surfaces. The most surprising result was the Rhea-Enceladus pair, which should have shown low similarity with Enceladus’ active surface and Rhea’s dormant one, but instead showed the highest similarity. This is explained by the fact that despite differences in surface activity, the moons share a water-ice composition and similarity in surface make-up, leading to similar spectral readings. Thus, SVM accuracy provides a data-driven, lightweight approach to capture spectral differences, working even with limited datasets.