PKG-Net: Physical Knowledge Feature-Guided Learning for Aircraft Detection in Optical Remote Sensing Images

Existing aircraft detection methods primarily rely on loss function constraints to guide the learning of end-to-end detectors, which significantly diverges from the judgment logic of human experts. Inspired by how human experts make decisions based on cognitive features, this article introduces the concept of physical knowledge features. Leveraging three properties of physical knowledge features, we identify the circle grayscale (CG) feature of aircraft and propose a physical knowledge-guided network (PKG-Net). By embedding CG features into the supervised learning process, the network improves its proficiency in learning stable features, thereby enhancing detection accuracy. Within this network, a multiscale circular frequency filter module (MS-CFFM) is responsible for extracting and integrating aircraft CG features across different scales. Adaptive channel selection module (ACSM) selectively activates channels for learning CG features. The hybrid attention feature fusion module (HAFFM) focuses intensively on the central localization of aircraft and deep reinforcement of channels. Experimental results on the RSOD and UCAS-AOD datasets demonstrate that the proposed method surpasses existing techniques in accuracy, achieving state-of-the-art performance.