An SOM-Hybrid Supervised Model for the Prediction of Underlying Physical Parameters from Near-Infrared Planetary Spectra

Near-Infrared reflectance spectra of planets can be used to infer surface parameters, sometimes with relevance to recent geologic history. Accurate prediction of parameters (such as composition, temperature, grain size, crystalline state, and dilution of one species within another) is often difficult because parameters manifest subtle but significant details in noisy spectral observations, because diverse parameters may produce similar spectral signatures, and because of the high dimensionality of the feature vectors (spectra). These challenges are often unmet by traditional inference methods. We retrieve two underlying causes of the spectral shapes, temperature and grain size, with an SOM-hybrid supervised neural prediction model. We achieve 83.0±2.7% and 100.0±0.0% prediction accuracy for temperature and grain size, respectively. The key to these high accuracies is the exploitation of an interesting antagonistic relationship between the nature of the physical parameters, and the learning mode of the SOM in the neural model.