THAT: Token-wise High-frequency Augmentation Transformer for Hyperspectral Pansharpening
Hongkun Jin, Hongcheng Jiang, Zejun Zhang, Yuan Zhang, Jia Fu, Tingfeng Li, Kai Luo

TL;DR
This paper introduces THAT, a transformer framework that enhances hyperspectral pansharpening by focusing on high-frequency details and reducing token redundancy, achieving state-of-the-art results.
Contribution
The paper proposes a novel Token-wise High-frequency Augmentation Transformer (THAT) with PTSA and MVFN modules for improved spectral-spatial feature modeling.
Findings
Achieves state-of-the-art performance on standard benchmarks.
Improves high-frequency detail preservation in hyperspectral images.
Enhances efficiency through token prioritization and variance-aware processing.
Abstract
Transformer-based methods have demonstrated strong potential in hyperspectral pansharpening by modeling long-range dependencies. However, their effectiveness is often limited by redundant token representations and a lack of multi-scale feature modeling. Hyperspectral images exhibit intrinsic spectral priors (e.g., abundance sparsity) and spatial priors (e.g., non-local similarity), which are critical for accurate reconstruction. From a spectral-spatial perspective, Vision Transformers (ViTs) face two major limitations: they struggle to preserve high-frequency components--such as material edges and texture transitions--and suffer from attention dispersion across redundant tokens. These issues stem from the global self-attention mechanism, which tends to dilute high-frequency signals and overlook localized details. To address these challenges, we propose the Token-wise High-frequency…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Remote-Sensing Image Classification · Image Enhancement Techniques
