Evaluating the sensory gap for earth observation images using human perception and an LDA-based computational model

High resolution Earth Observation (EO) images contain detailed information, making it possible to recognize objects. However, issues such as the sensory gap (the difference between a real life scene and its sensory interpretation) cause difficulties for object recognition. In EO, this gap is rather wide due to sensor resolution, image perspective, scale and field of view (FOV). In this work, human perceptual and computational evaluations of the sensory gap are presented. For the human perceptual evaluation, user labels describing image patch content are gathered and analyzed. Results highlight issues caused by the sensory gap, e.g., FOV (image patch size) limits the contextual clues which can be used to disambiguate objects. The effect of FOV is then computationally analyzed as the difference between the scene context discovered by Latent Dirichlet Allocation from content within a certain FOV and the ground truth. Results indicate that increasing the FOV decreases the sensory gap.