IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2025)
Kolmogorov–Arnold–Enhanced Nonlinear Expansions for Fine-Grained Feature Amplification in Robust Near-Shore SAR Vessel Discrimination
Abstract
Ship detection based on synthetic aperture radar (SAR) is crucial for maritime surveillance, yet existing methods struggle with two critical challenges: 1) missed detections in nearshore scenarios due to highly similar radar reflections between ships and shore objects (e.g., buildings, docks); and 2) feature vanishing of small ships caused by SAR resolution limits and downsampling operations. To address these issues, we propose Kolmogorov–Arnold–Enhanced YOLO (KaneYOLO), a novel SAR ship detector designed for robust discrimination in complex nearshore scenes and enhanced small-target detection. First, we introduce KAN Block, a feature extraction module leveraging the Kolmogorov–Arnold theorem to model complex nonlinear relationships in SAR images. By integrating Gram polynomials and B-spline-based activation functions, KAN Block dynamically calibrates feature responses to suppress background clutter (e.g., shore structures) while amplifying subtle intensity patterns and edge transitions unique to ships. Second, we design a Feature Fusion Allocation Structure with parallelized deep convolutions to aggregate multiscale features. This structure preserves small-target details by propagating enriched semantic information across detection layers, mitigating feature loss during downsampling. Finally, a Detail Enhancement Detection Head is proposed to reduce computational overhead through shared convolutional layers, while enhancing local feature utilization via direction-sensitive convolutions and group normalization. KaneYOLO achieved 93.9% AP on High-Resolution SAR Images Dataset and 98.3% AP on SAR Ship Detection Dataset. These results validate its robustness in handling SAR-specific challenges, offering significant potential for real-world maritime security applications.
Keywords
