Progressive class-aware instance enhancement for aircraft detection in remote sensing imagery

Abstract

Aircraft detection and type identification in optical remote sensing imagery are critical for civilian and military applications, including air traffic control and strategic surveillance. However, existing methods ignore the unique cross-shaped geometric structure and low spatial occupancy of aircraft, leading to inaccurate localization and category confusion. In response, this paper proposes a novel anchor-free detection network that leverages point set representation, integrating the progressive class-aware dual branches (PCA-DB) and instance-guided enhancement module (IGEM). Specifically, considering the underlying structure of aircraft, PCA-DB consists of the coarse foreground instance branch and the refined cross-shaped branch to facilitate high-quality point set generation. Through multi-task learning, the auxiliary branches implicitly inject geometric priors into shared features, effectively suppressing background interference. Subsequently, IGEM introduces the interactive attention mechanism to adaptively fuse the instance-level information in the auxiliary branch with features in the main branches, explicitly enhancing the discriminative features of aircraft. Extensive experiments validate the superior performance of the proposed method on several aircraft datasets, including MAR20, FAIR1M-Plane, and CORS-ADD. There are 5.42%, 4.28%, and 1.37% improvements in mAP in our method compared to the baseline network.