Infrared remote sensing is indispensable for aerial target detection due to its all-weather thermal radiation sensing capability. Integrating visible light’s high resolution with infrared’s robustness to environmental interference represents an advanced strategy to improve segmentation accuracy. Current challenges of the strategy include: 1) degradation of infrared images due to background noise; 2) cross-modal feature mismatches caused by temperature disparities between thermal and spectral data; 3) insufficient annotated datasets restricting model generalization. To address these challenges, this paper proposes a temperature-aware dynamic fusion network for few-shot infrared image segmentation algorithm. The method significantly improves segmentation accuracy through two key innovations: temperature-guided dual-modal feature fusion and thermodynamic few-shot transfer mechanisms. Specifically, in temperature-guided dual-modal feature fusion, Dual-stream Pyramid Enhancement Module (DPEM) is designed to suppress noise and boost contrast in infrared image. Building upon this, Temperature-guided Dual-modal Encoder is constructed to dynamically fuse visible light and Infrared features. In thermodynamic few-shot transfer, Thermodynamic Adversarial Alignment can obtain dual-modal features with discriminative information in few-shot transfer. Then, Thermodynamic Pseudo-Labeling Enhancement is created to improve pseudo-label quality by Temperature Confidence Propagation. Numerous experimental results performed on a multi-scenario dataset with 12,800 visible-infrared aircraft images have validated the method’s superiority, achieving SOTA performance with minimum gains of 7.07% mPA, 6.48% mF1, and 7.93% mIoU over existing methods like GANFuse and TransFusion. This framework advances explainable infrared visual understanding in complex environments.

Accurate aircraft-region segmentation in aerial infrared imagery can provide a useful scene-understanding component for image-assisted aviation inspection. However, target scale variation and ambiguous boundaries remain challenging in simulated aerial infrared scenes. To address these issues, this paper develops a bidirectional collaborative framework that couples global semantic fusion with edge optimization. The Semantics-guided Multi-scale Context Fusion branch uses adaptive multi-scale feature extraction and cross-stage calibration to produce a reliable initial segmentation, while the Edge-Sensitive Superpixel Collaborative Refinement branch improves boundary localization through semantic-guided superpixel consistency. Their interaction forms a bidirectional closed loop in which semantic context guides edge refinement and refined boundaries feed back to semantic learning. Experiments on AISD, a dedicated physics-based simulated aerial infrared dataset, achieve an 86.29% mPA, 83.35% mF1, and 76.20% mIoU. Relative to the strongest baseline, P-Mamba, the gains are 0.89 percentage points in mPA, 1.52 points in mF1, and 0.88 points in mIoU, and a sourcewise holdout comparison indicates consistent performance under unseen source content within the AISD simulation pipeline. These results concern aircraft-region segmentation rather than direct diagnosis of thermal defects.