THAT: Token-wise High-frequency Augmentation Transformer for Hyperspectral Pansharpening

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 Augmentation Transformer (THAT), a novel framework designed to enhance hyperspectral pansharpening through improved high-frequency feature representation and token selection. Specifically, THAT introduces: (1) Pivotal Token Selective Attention (PTSA) to prioritize informative tokens and suppress redundancy; (2) a Multi-level Variance-aware Feed-forward Network (MVFN) to enhance high-frequency detail learning. Experiments on standard benchmarks show that THAT achieves state-of-the-art performance with improved reconstruction quality and efficiency. The source code is available at https://github.com/kailuo93/THAT.