GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening
Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) and panchromatic (PAN) observations. Most existing deep learning methods treat pansharpening as fixed-grid prediction, which limits scale adaptation. To address this, we propose GSPan, a framework that introduces 2D Gaussian Splatting (GS) into pansharpening. Instead of directly predicting pixels, GSPan represents band-wise residual details as continuous and learnable 2D Gaussian primitives. We design a Dual-Stream Hierarchical Interaction (DSHI) architecture with a Spatial-Spectral Interactive Attention (SSIA) module to estimate these primitives from complementary PAN ...
| Authors | Fangyi Li, Xiaoyuan Yang, Yixiao Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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