Geospatial Contextual Prior-Enabled Knowledge Reasoning Framework for Fine-Grained Aircraft Detection in Panoramic SAR Imagery

Fine-grained aircraft detection from synthetic aperture radar (SAR) imagery is of significance in transportation and military domains. Based on the prior knowledge that aircraft are frequently found in airports, current research adopts airport-to-aircraft detection pipelines for aircraft detection and classification in panoramic SAR images. Geospatial information, including the approximate airport location and the spatial relationships between the airport and aircraft, represents valuable supplementary information that can enhance fine-grained aircraft detection performance. However, due to unreliable geographical information and the limited perceptual field, it is challenging to detect and classify aircraft in panoramic SAR imagery. To address this, a novel geospatial contextual prior-enabled knowledge reasoning framework is proposed. First, an unsupervised and lightweight geospatial-driven airport detection (AD) method is presented by combining geographic information matching and optical-to-SAR image registration, which can quickly locate airports and narrow the scope for fine detection. Then, an improved real-time model for object detection with a recursive-gated spatial interaction module (RTMDet-RSIM) is proposed for fine-grained aircraft detection. RTMDet-RSIM uses frequency-domain analysis to improve global information modeling ability without excessive computational cost. Finally, a relational prior-based reasoning strategy is proposed by modeling the geospatial category relationships from historical images to strengthen classification performance. Experiments on 61 panoramic SAR images covering 13 aircraft categories show that the proposed method achieves high detection accuracy with a mean average precision (mAP) of 81.2%, while the average test time for an image size of $8738\times 7636$ is about 8.58 s. The source code and dataset will be released.