SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for Pan-Sharpening

• URL: http://arxiv.org/abs/2602.04473v1
• Date: Wed, 04 Feb 2026 12:01:07 GMT
• Title: SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for Pan-Sharpening
• Authors: Junjie Li, Congyang Ou, Haokui Zhang, Guoting Wei, Shengqin Jiang, Ying Li, Chunhua Shen,
• Abstract summary: SALAD-Pan is a sensor-agnostic latent space diffusion method for efficient pansharpening.<n>It trains a band-wise single-channel VAE to encode high-resolution multispectral images into compact latent representations.<n>It achieves high-precision fusion in the diffusion process and robust zero-shot (cross-sensor) capability.
• Score: 50.44337053599724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, diffusion models bring novel insights for Pan-sharpening and notably boost fusion precision. However, most existing models perform diffusion in the pixel space and train distinct models for different multispectral (MS) imagery, suffering from high latency and sensor-specific limitations. In this paper, we present SALAD-Pan, a sensor-agnostic latent space diffusion method for efficient pansharpening. Specifically, SALAD-Pan trains a band-wise single-channel VAE to encode high-resolution multispectral (HRMS) into compact latent representations, supporting MS images with various channel counts and establishing a basis for acceleration. Then spectral physical properties, along with PAN and MS images, are injected into the diffusion backbone through unidirectional and bidirectional interactive control structures respectively, achieving high-precision fusion in the diffusion process. Finally, a lightweight cross-spectral attention module is added to the central layer of diffusion model, reinforcing spectral connections to boost spectral consistency and further elevate fusion precision. Experimental results on GaoFen-2, QuickBird, and WorldView-3 demonstrate that SALAD-Pan outperforms state-of-the-art diffusion-based methods across all three datasets, attains a 2-3x inference speedup, and exhibits robust zero-shot (cross-sensor) capability.

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