Progressive Context-Dependent Inference for Object Detection in Remote Sensing Imagery

Inspired by our observation that numerous objects of remote sensing imageries are extremely consistent in geometric characteristics (e.g., object sizes/angles/layouts), in this work, we propose a novel Progressive Context-dependent Inference (PCI) method to make full use of large-scope contextual cues for better localizing objects in remote sensing imagery. Especially, to represent candidate objects and their geometric distributions, we build all of them into candidate object graphs, and subsequently perform inference learning by diffusing contextual object information. To make the inference more credible, we progressively accumulate these historical learning experiences on both label prediction and location regression processes into the next stage of network evolution, where topology structures and attributes of candidate object graphs would be dynamically updated. The graph update and ground object detection are jointly encapsulated as a closed-looping learning process. Hereby the problem of multi-object localization is converted into a progressive construction of dynamic graphs. Extensive experiments on three public datasets demonstrate the superiority of our proposed method over other state-of-the-art methods for ground object detection in remote sensing imagery.