Information Fusion with Knowledge Distillation for Fine-grained Remote Sensing Object Detection

Fine-grained remote sensing object detection aims to locate and identify specific targets with variable scale and orientation from complex background in the high-resolution and wide-swath images, which needs requirement of high precision and real-time processing simultaneously. Although traditional knowledge distillation technology show its effectiveness in model compression and accuracy preservation for natural images, the challenges of heavy background noise and intra-class similarity faced by remote sensing images limits the knowledge quality of teacher model and the learning ability of student model. To address these issues, we propose the Information Fusion with Knowledge Distillation (IFKD) method to enhance student model performance by integrating information from external images, frequency domain, and hyperbolic space. This includes three key modules: 1) External Disturbance Enhancement (EDE), which uses MobileSAM to enrich teachers' knowledge and reduce students' dependency on teachers; 2) Frequency Domain Reconstruction (FDR) to amplify key feature representations and reduce background noise interference by resampling low-frequency information; 3) Hyperbolic Similarity Mask (HSM) to increase intra-class differences, guiding students in analyzing and utilizing teachers' knowledge, and leveraging the exponential capabilities of hyperbolic space for performance improvement. Experimental results verify that the IFKD method significantly enhances performance in fine-grained recognition tasks compared to existing distillation techniques. Specially, 65.8% and 81.4% Ap_50 have achieved on optical ShipRSImageNet and SAR Aircraft-1.0 with our method, even which is 0.4% and 4.7% higher than the teacher.