Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark

• URL: http://arxiv.org/abs/2603.00543v1
• Date: Sat, 28 Feb 2026 08:44:34 GMT
• Title: Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark
• Authors: Ke Cao, Xuanhua He, Xueheng Li, Lingting Zhu, Yingying Wang, Ao Ma, Zhanjie Zhang, Man Zhou, Chengjun Xie, Jie Zhang,
• Abstract summary: Pansharpening aims to generate high-resolution multi-spectral images by fusing the spatial detail of panchromatic images with the spectral richness of low-resolution MS data.<n>Existing methods are evaluated under limited, low-resolution settings, limiting their generalization to real-world, high-resolution scenarios.<n>We introduce PanScale, the first large-scale, cross-scale pansharpening dataset, accompanied by PanScale-Bench, a benchmark for evaluating generalization across varying resolutions and scales.
• Score: 39.78977567741962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pansharpening aims to generate high-resolution multi-spectral images by fusing the spatial detail of panchromatic images with the spectral richness of low-resolution MS data. However, most existing methods are evaluated under limited, low-resolution settings, limiting their generalization to real-world, high-resolution scenarios. To bridge this gap, we systematically investigate the data, algorithmic, and computational challenges of cross-scale pansharpening. We first introduce PanScale, the first large-scale, cross-scale pansharpening dataset, accompanied by PanScale-Bench, a comprehensive benchmark for evaluating generalization across varying resolutions and scales. To realize scale generalization, we propose ScaleFormer, a novel architecture designed for multi-scale pansharpening. ScaleFormer reframes generalization across image resolutions as generalization across sequence lengths: it tokenizes images into patch sequences of the same resolution but variable length proportional to image scale. A Scale-Aware Patchify module enables training for such variations from fixed-size crops. ScaleFormer then decouples intra-patch spatial feature learning from inter-patch sequential dependency modeling, incorporating Rotary Positional Encoding to enhance extrapolation to unseen scales. Extensive experiments show that our approach outperforms SOTA methods in fusion quality and cross-scale generalization. The datasets and source code are available upon acceptance.

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