Progressive Alignment Degradation Learning for Pansharpening

• URL: http://arxiv.org/abs/2506.20179v1
• Date: Wed, 25 Jun 2025 07:07:32 GMT
• Title: Progressive Alignment Degradation Learning for Pansharpening
• Authors: Enzhe Zhao, Zhichang Guo, Yao Li, Fanghui Song, Boying Wu,
• Abstract summary: Deep learning-based pansharpening has been shown to effectively generate high-resolution multispectral (HRMS) images.<n>The Wald protocol assumes that networks trained on artificial low-resolution data will perform equally well on high-resolution data.<n>We proposePADM, which uses mutual iteration between two sub-networks, PAlignNet and PDegradeNet, to adaptively learn accurate degradation processes.
• Score: 3.7939736380306552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based pansharpening has been shown to effectively generate high-resolution multispectral (HRMS) images. To create supervised ground-truth HRMS images, synthetic data generated using the Wald protocol is commonly employed. This protocol assumes that networks trained on artificial low-resolution data will perform equally well on high-resolution data. However, well-trained models typically exhibit a trade-off in performance between reduced-resolution and full-resolution datasets. In this paper, we delve into the Wald protocol and find that its inaccurate approximation of real-world degradation patterns limits the generalization of deep pansharpening models. To address this issue, we propose the Progressive Alignment Degradation Module (PADM), which uses mutual iteration between two sub-networks, PAlignNet and PDegradeNet, to adaptively learn accurate degradation processes without relying on predefined operators. Building on this, we introduce HFreqdiff, which embeds high-frequency details into a diffusion framework and incorporates CFB and BACM modules for frequency-selective detail extraction and precise reverse process learning. These innovations enable effective integration of high-resolution panchromatic and multispectral images, significantly enhancing spatial sharpness and quality. Experiments and ablation studies demonstrate the proposed method's superior performance compared to state-of-the-art techniques.

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