Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution

• URL: http://arxiv.org/abs/2608.15423v1
• Date: Sat, 15 Aug 2026 21:53:36 GMT
• Title: Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution
• Abstract summary: Sea surface temperature (SST) is a critical indicator of global climate change.<n>Satellite-derived SST imagery often suffers from coarse spatial resolution.<n>We propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution.
• Score: 42.9772138237073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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