PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening

• URL: http://arxiv.org/abs/2505.23367v2
• Date: Tue, 15 Jul 2025 06:54:06 GMT
• Title: PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening
• Authors: Jeonghyeok Do, Sungpyo Kim, Geunhyuk Youk, Jaehyup Lee, Munchurl Kim,
• Abstract summary: We propose PAN-Crafter, a modality-consistent alignment framework.<n>At its core, Modality-Adaptive Reconstruction (MARs) enables a single network to jointly reconstruct HRMS and PAN images.<n> experiments on multiple benchmark datasets demonstrate that our PAN-Crafter outperforms the most recent state-of-the-art method in all metrics.
• Score: 20.43260906326048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PAN-sharpening aims to fuse high-resolution panchromatic (PAN) images with low-resolution multi-spectral (MS) images to generate high-resolution multi-spectral (HRMS) outputs. However, cross-modality misalignment -- caused by sensor placement, acquisition timing, and resolution disparity -- induces a fundamental challenge. Conventional deep learning methods assume perfect pixel-wise alignment and rely on per-pixel reconstruction losses, leading to spectral distortion, double edges, and blurring when misalignment is present. To address this, we propose PAN-Crafter, a modality-consistent alignment framework that explicitly mitigates the misalignment gap between PAN and MS modalities. At its core, Modality-Adaptive Reconstruction (MARs) enables a single network to jointly reconstruct HRMS and PAN images, leveraging PAN's high-frequency details as auxiliary self-supervision. Additionally, we introduce Cross-Modality Alignment-Aware Attention (CM3A), a novel mechanism that bidirectionally aligns MS texture to PAN structure and vice versa, enabling adaptive feature refinement across modalities. Extensive experiments on multiple benchmark datasets demonstrate that our PAN-Crafter outperforms the most recent state-of-the-art method in all metrics, even with 50.11$\times$ faster inference time and 0.63$\times$ the memory size. Furthermore, it demonstrates strong generalization performance on unseen satellite datasets, showing its robustness across different conditions.

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