In this paper we study methods for estimation of divergence measures from data sampled from statistical manifolds. We consider data manifolds arising from noisy sensor observation of objects with varying pose and articulation and study performance of projection methods  in high dimensional setting. As an application of the proposed methods, Fisher Information estimates of pose is computed for Synthetic Aperture Radar (SAR) imagery of targets.