Few-shot remote-sensing fine-grained ship classification (RS-FGSC) faces two coupled challenges: limited annotated samples and mixed-visibility imaging conditions caused by cloud occlusion. Although CLIP-based prompt learning provides useful transfer priors, conventional single-branch adaptation can encounter an over-correction dilemma: robust compensation applied globally may degrade clear samples, whereas clear-image optimization may fail on occluded samples. We propose CADP (Cloud-Aware Dual-Path Prompt Learning), which decouples clear and occluded processing through learnable routing. CADP contains three components: (1) a cloud detector (CloudDetector) trained with auxiliary cloud-state labels for instance-level routing, (2) a fine-grained adapter (FineGrainedAdapter) that preserves discriminative details for clear samples, and (3) a robust compensation path using occlusion-aware prompting (AOPD) from CARP for occluded samples. To evaluate mixed-visibility scenarios, we construct Mixed-Sky benchmarks by combining clear ship images with SeaCloud-Ship cloud-occluded samples introduced by CARP, using controlled cloud-mixed ratios (25%, 50%, and 75%) and a non-overlapping sampling strategy. Experiments from 1-shot to 16-shot show consistent gains over CoCoOp, CLIP-Adapter, and prior robust prompting methods. CADP achieves 35.49% average accuracy, improving the best-performing baseline in our protocol by +4.81 points (+15.7% relative improvement). Component ablations, routing controls, and attention visualizations indicate that explicit routing reduces negative transfer between clear and occluded samples.

Ship classification in optical remote sensing requires balancing discriminative representation and model efficiency. Standard convolutional neural network (CNN) bottlenecks rely on local spatial kernels and may emphasize high-frequency texture cues, while stronger backbones increase parameter cost. We propose a frequency-aware lightweight bottleneck (FALB) that couples enhanced wavelet convolution (WTsConv) and contextual anchor attention (CAA) in a cascaded design. WTsConv adopts Sym4 wavelets and a learnable symmetric fusion weight between spatial and wavelet-reconstructed features to improve frequency-aware feature mixing. CAA is then applied to the refined features for contextual aggregation. Integrated into ResNet-50 bottlenecks, FALB is evaluated on FGSCM-52 and achieves 97.88% top-1 accuracy with 17.78 M parameters, compared with 96.92% and 25.56 M for the ResNet-50 baseline, surpassing ResNet-50 by 0.96% and outperforming compared general-purpose baselines while reducing parameters by 30.4%. Under this experimental setting, FALB improves the observed accuracy–parameter trade-off for remote sensing ship classification.