This paper introduces the Wavelet-Enhanced Swin Transformer Network (WSC-Net), a novel dual-branch architecture that resolves the inherent tradeoff between global spatial contextual and fine-grained spectral details in hyperspectral image (HSI) classification. While transformer-based models excel at capturing long-range dependencies, their patch-based nature often overlooks intra-patch high-frequency details, hindering the discrimination of spectrally similar classes. Our framework synergistically couples a two-stage Swin transformer with a parallel Wavelet Transform Module (WTM) for local frequency information capture. To address the semantic gap between spatial and frequency domains, we propose the Cross-Domain Attention Fusion (CDAF) module—a bi-directional attention mechanism that facilitates intelligent feature exchange between the two streams. CDAF explicitly models cross-domain dependencies, amplifies complementary features, and suppresses noise through attention-guided integration. Extensive experiments on four benchmark datasets demonstrate that WSC-Net consistently outperforms state-of-the-art methods, confirming its effectiveness in balancing global contextual modeling with local detail preservation.

High-resolution range profile (HRRP) target recognition has garnered significant attention in radar automatic target recognition (RATR) research for its rich structural information and low computational costs. With the rapid advancements in deep learning, methods for HRRP target recognition that leverage deep neural networks have emerged as the dominant approaches. Nevertheless, these traditional closed-set recognition methods require labeled data for every class in training, while in reality, seen classes and unseen classes coexist. Therefore, it is necessary to explore methods that can identify both seen and unseen classes simultaneously. To this end, a semantic- and physical-guided generative network (SPGGN) was innovatively proposed for HRRP generalized zero-shot recognition; it combines a constructed knowledge graph with attribute vectors to comprehensively represent semantics and reconstructs strong scattering points to introduce physical constraints. Specifically, to boost the robustness, we reconstructed the strong scattering points from deep features of HRRPs, where class-aware contrastive learning in the middle layer effectively mitigates the influence of target-aspect variations. In the classification stage, discriminative features are produced through attention-based feature fusion to capture multi-faceted information, while the design of balancing loss abates the bias towards seen classes. Experiments on two measured aircraft HRRP datasets validated the superior recognition performance of our method.

Growing demands for maritime security and navigational safety have made video surveillance indispensable for monitoring distant ship targets. Visible-thermal fusion provides a robust solution across varying weather and illumination conditions; however, tiny targets remain highly sensitive to positional errors, and cross-modal misalignment caused by occlusion, low illumination, and cluttered scenes further degrades detection accuracy. To address these issues, we propose CPSF-Net, a confidence-chained, position-sensitive and scale-aware fusion network for visible-thermal tiny ship detection. A Position-Sensitive Multimodal Fusion (PMF) module enhances and fuses pre-aligned visible-thermal features with possible residual local misalignment through cross-attention and derives a position-sensitive token-level matching confidence directly from the attention weights without introducing additional parameters. A Confidence-Guided Sparse Attention (CSA) module uses this confidence to adaptively suppress redundant cross-modal correlations and improve positional awareness. Finally, an adaptive Scale-Weighted Loss (SWL) balances Intersection over Union (IoU) and Normalized Wasserstein Distance (NWD) according to object scale and matching confidence, providing more stable localization supervision across object scales. On the AISail and RGBT-Tiny datasets, CPSF-Net achieves 92.1% and 84.2% ${\mathrm{mAP}}_{50}$ from unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) perspectives, respectively. These results show that the proposed method performs consistently in challenging maritime environments.