Abstract
Ultra-fine-grained ship recognition is a core task in optical remote sensing maritime situational awareness. Two fundamental bottlenecks persist: (1) discriminative microstructures (e.g., radar antennas, superstructures, vertical launch systems) are severely diluted or lost during repeated downsampling; (2) high-frequency background noise from sea foam, port edges and storage facilities is spectrally entangled with target signals, causing severe inter-class feature confusion. To address these issues, we propose the context modulation network (CM-Net), which unifies detail enhancement and background suppression as sequential stages within a single feature propagation pathway. Discriminative high-frequency information is first recovered in shallow-to-intermediate layers, after which uncertainty-aware modulation suppresses residual background responses in deeper layers, forming a two-stage frequency enhancement and background purification framework. Specifically, the Ultra-Fine-Grained Feature Enhancement Module (UFG-FEM) recovers multi-scale discriminative high-frequency details in shallow-to-intermediate layers, enriching representations for deeper stages. The Dual-Branch Context Modulation Module (DB-CMM) subsequently models background uncertainty via a Beta evidential distribution and selectively suppresses background interference through optimal transport-based soft alignment and an information bottleneck, forming a sequential pipeline with clear functional division. We also build the FGSC-72 dataset with 72 model-level categories and ~13,000 images. On FGSC-72, CM-Net achieves 97.49% Top-5 accuracy, 91.67% macro-Precision, 94.69% mean Average Precision (mAP), and 92.01% macro-averaged F1 score, with a 4.64-percentage-point improvement in mAP over Oriented R-CNN. Across five representative backbones, it improves mAP by 4.60–7.11 points, indicating consistent cross-architecture applicability.