发表机构
Ocean University of China; Shandong Academy of Sciences; Mississippi State University（中国海洋大学; 山东省科学院; 密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对卫星反演海表温度影像分辨率较粗的问题，提出双分支状态位移网络，经多公开数据集验证，其性能优于现有最先进方法。
AI 中文摘要
海表温度（SST）是全球气候变化的关键指标，但卫星反演的SST影像常存在空间分辨率较粗的问题，限制了对海洋锋等精细尺度热结构的捕捉能力。为解决该问题，本文提出一种用于SST超分辨率的双分支状态位移网络（DBSD-Net）。DBSD-Net采用双分支架构：其一为小波频率分支，通过离散小波变换显式分离低频与高频分量以进行针对性处理；其二为VGGUNet分支，从冻结的预训练VGG主干中提取多尺度语义特征。在小波分支内，本文引入带门控结构细化（GSR）单元的结构状态空间模块（SSSM），以高效捕捉长程依赖并增强结构完整性；还引入位移门控模块（DGM），学习位移场以实现对高频细节的几何感知调制，从而缓解空间变化的退化问题。在多个公开SST数据集上开展的实验表明，DBSD-Net的性能优于现有最先进方法。
英文摘要
Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability to capture fine-scale thermal structures such as ocean fronts. To address this, we propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution. DBSD-Net adopts a dual-branch architecture: a wavelet frequency branch that explicitly separates low and high-frequency components via discrete wavelet transform for targeted processing, and a VGGUNet branch that extracts multi-scale semantic features from a frozen pre-trained VGG backbone. Within the wavelet branch, we introduce a Structural State Space Module (SSSM) with a Gated Structure Refinement (GSR) unit to efficiently capture long-range dependencies and enhance structural integrity, and a Displacement Gate Module (DGM) that learns a displacement field for geometry-aware modulation of high-frequency details, thereby mitigating spatially varying degradation. Experiments on multiple public SST datasets demonstrate that DBSD-Net outperforms existing state-of-the-art methods.
CommentsAccepted for publication in IEEE JSTARS
