ARNet: Prior Knowledge Reasoning Network for Aircraft Detection in Remote-Sensing Images

Amidst the landscape of contemporary remote-sensing (RS) technology, the endeavor to detect and recognize aircraft within RS images (RSIs) assumes pivotal strategic and practical significance. The complex nature of fine-grained aircraft recognition is a result of the intricate interplay between aircraft and their background environments, alongside category imbalance, which collectively leads to the emergence of a long-tail distribution within the dataset. However, experts proficient in RSI interpretation can effectively address these challenges through the application of prior knowledge. This article introduces the aircraft reasoning network (ARNet), a framework tailored for aircraft detection and fine-grained recognition in RSIs, building upon prior knowledge employed in expert interpretation. Specifically, the knowledge reasoning module (KRM) introduces a knowledge graph (KG) that incorporates both common and expert knowledge into the end-to-end network. Additionally, the network encompasses a spatial context module (SCM) and an airport facility relationship module (AFRM). These components facilitate highly accurate detection and recognition of fine-grained aircraft in diverse environmental contexts by employing adaptive prior knowledge reasoning and optimizing target spatial location. Furthermore, an independent aircraft component discrimination module (ACDM) distinguishes aircraft based on their predominant component features, contributing to improved classification performance in both the few-shot and easily confused categories. Moreover, this article introduces the AR-RSI dataset, a compilation of RSIs capturing fine-grained aircraft targets from diverse locations. The effectiveness and superiority of ARNet are exemplified by AR-RSI, achieving a minimum of 3.7% higher mean average precision (mAP) than the mainstream aircraft detection framework.