A hierarchical and contextual model for aerial image understanding

In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene and object levels to handle the difficult task of representing scene elements at different scales and add contextual constraints to resolve ambiguities in the scene interpretation. This allows the model to rule out inconsistent detections, like cars on trees, and to verify low probability detections based on their local context, such as small cars in parking lots. We also present a two-step algorithm for parsing aerial images that first detects object-level elements like trees and parking lots using color histograms and bag-of-words models, and objects like roofs and roads using compositional boosting, a powerful method for finding image structures. We then activate the top-down scene model to prune false positives from the first stage. We learn this scene model in a minimax entropy framework and show unique samples from our prior model, which capture the layout of scene objects. We present experiments showing that hierarchical and contextual information greatly reduces the number of false positives in our results.