Automated quantitative measurements and associated error covariances for planetary image analysis

This paper presents a flexible approach for extracting measurements from planetary images based upon the newly developed linear Poisson models technique. The approach has the ability to learn surface textures then estimate the quantity of terrains exhibiting similar textures in new images. This approach is suitable for the estimation of dune field coverage or other repeating structures. Whilst other approaches exist, this method is unique for its incorporation of a comprehensive error theory, which includes contributions to uncertainty arising from training and subsequent use. The error theory is capable of producing measurement error covariances, which are essential for the scientific interpretation of measurements, i.e. for the plotting of error bars. In order to apply linear Poisson models, we demonstrate how terrains can be described using histograms created using a ‘Poisson blob’ image representation for capturing texture information. The validity of the method is corroborated using Monte Carlo simulations. The potential of the method is then demonstrated using terrain images created from bootstrap re-sampling of martian HiRISE data.