Universal Pansharpening Foundation Model

• URL: http://arxiv.org/abs/2603.03831v1
• Date: Wed, 04 Mar 2026 08:30:15 GMT
• Title: Universal Pansharpening Foundation Model
• Authors: Hebaixu Wang, Jing Zhang, Haonan Guo, Di Wang, Jiayi Ma, Bo Du, Liangpei Zhang,
• Abstract summary: Pansharpening generates the high-resolution multi-spectral (MS) image by integrating spatial details from a texture-rich panchromatic (PAN) image and spectral attributes from a low-resolution MS image.<n>We present FoundPS, a universal pansharpening foundation model for satellite-agnostic and scene-robust fusion.
• Score: 67.10467574892282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pansharpening generates the high-resolution multi-spectral (MS) image by integrating spatial details from a texture-rich panchromatic (PAN) image and spectral attributes from a low-resolution MS image. Existing methods are predominantly satellite-specific and scene-dependent, which severely limits their generalization across heterogeneous sensors and varied scenes, thereby reducing their real-world practicality. To address these challenges, we present FoundPS, a universal pansharpening foundation model for satellite-agnostic and scene-robust fusion. Specifically, we introduce a modality-interleaved transformer that learns band-wise modal specializations to form reversible spectral affine bases, mapping arbitrary-band MS into a unified latent space via tensor multiplication. Building upon this, we construct a latent diffusion bridge model to progressively evolve latent representations, and incorporate bridge posterior sampling to couple latent diffusion with pixel-space observations, enabling stable and controllable fusion. Furthermore, we devise infinite-dimensional pixel-to-latent interaction mechanisms to comprehensively capture the cross-domain dependencies between PAN observations and MS representations, thereby facilitating complementary information fusion. In addition, to support large-scale training and evaluation, we construct a comprehensive pansharpening benchmark, termed PSBench, consisting of worldwide MS and PAN image pairs from multiple satellites across diverse scenes. Extensive experiments demonstrate that FoundPS consistently outperforms state-of-the-art methods, exhibiting superior generalization and robustness across a wide range of pansharpening tasks.

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