Kernel Space Diffusion Model for Efficient Remote Sensing Pansharpening

• URL: http://arxiv.org/abs/2505.18991v1
• Date: Sun, 25 May 2025 06:25:31 GMT
• Title: Kernel Space Diffusion Model for Efficient Remote Sensing Pansharpening
• Authors: Hancong Jin, Zihan Cao, Liangjian Deng,
• Abstract summary: Kernel Space Diffusion Model (KSDiff) is a novel approach that leverages diffusion processes in a latent space to generate convolutional kernels enriched with global contextual information.<n> Experiments on three widely used datasets, including WorldView-3, GaoFen-2, and QuickBird, demonstrate the superior performance of KSDiff both qualitatively and quantitatively.
• Score: 8.756657890124766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pansharpening is a fundamental task in remote sensing that integrates high-resolution panchromatic imagery (PAN) with low-resolution multispectral imagery (LRMS) to produce an enhanced image with both high spatial and spectral resolution. Despite significant progress in deep learning-based approaches, existing methods often fail to capture the global priors inherent in remote sensing data distributions. Diffusion-based models have recently emerged as promising solutions due to their powerful distribution mapping capabilities; however, they suffer from significant inference latency, which limits their practical applicability. In this work, we propose the Kernel Space Diffusion Model (KSDiff), a novel approach that leverages diffusion processes in a latent space to generate convolutional kernels enriched with global contextual information, thereby improving pansharpening quality while enabling faster inference. Specifically, KSDiff constructs these kernels through the integration of a low-rank core tensor generator and a unified factor generator, orchestrated by a structure-aware multi-head attention mechanism. We further introduce a two-stage training strategy tailored for pansharpening, enabling KSDiff to serve as a framework for enhancing existing pansharpening architectures. Experiments on three widely used datasets, including WorldView-3, GaoFen-2, and QuickBird, demonstrate the superior performance of KSDiff both qualitatively and quantitatively. Code will be released upon possible acceptance.

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