A Hierarchical Context Embedding Network for Object Detection in Remote Sensing Images

Compared with general optical images, remote sensing images (RSIs) capture large areas from high altitudes with a bird’s eye view, which is responsible for the many categories and scale variations of objects in the images, as well as the abundant scene information. Although the complexity of the RSIs presents a significant challenge to the object detection task, the complexity presents opportunities as well. The RSIs contain plenty of object-related context information, which is valuable for boosting the object detection performance. To address the existing issue of poor context utilization in RSIs, we propose a hierarchical context embedding network (HCENet) in this letter. First, we construct a semantic feature pyramid, in which the semantic context aggregation module (SFAM) integrates the semantic contexts included in the adjacent layers of features with a novel feature fusion mechanism. Furthermore, the scene-level context embedding module (SLCEM) extracts the scene context of the overall image by a simple design and is utilized to guide feature classification. Finally, we outperform the popular object detectors on the publicly available DOTA-v1.5 dataset, achieving superior performance.