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Self-Expressive High-Order Tensor Unrolling Network for Unsupervised Hyperspectral and Multispectral Image Fusion.

He Wang, Yang Xu, Zhihui Wei

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 27, 2026

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This study introduces a new unsupervised method for fusing hyperspectral and multispectral images, improving spatial-spectral quality. The Self-Expressive High-Order Tensor Unrolling Network (SHOTUN) enhances interpretability and preserves spatial structures in fused images.

Area of Science:

Remote Sensing
Computer Vision
Data Fusion

Background:

Hyperspectral and multispectral image fusion (HMF) aims to enhance spatial-spectral quality by combining low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI).
Existing fusion methods face challenges, including disruption of spatial consistency in tensor-based approaches and lack of interpretability in deep learning methods.
There is a need for unsupervised HMF methods that preserve spatial structures and offer interpretability.

Purpose of the Study:

To propose a novel unsupervised hyperspectral and multispectral image fusion method named Self-Expressive High-Order Tensor Unrolling Network (SHOTUN).
To address the limitations of existing fusion techniques, particularly regarding spatial consistency and interpretability.
To improve the generalization capabilities of fusion models across different sensors.

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Main Methods:

Developed a Self-Expressive High-Order Tensor Unrolling Network (SHOTUN) within a sparse core tensor decomposition framework.
Introduced self-expressive relationships among image patches for high-order mode representation to preserve spatial structure.
Employed an alternative optimizing strategy with dedicated modules for an interpretable end-to-end training pipeline.
Incorporated a pre-training strategy for unsupervised training to enhance the estimation of unknown degraded parameters.

Main Results:

The proposed SHOTUN method effectively fuses hyperspectral and multispectral images, enhancing spatial-spectral quality.
Experimental results on simulated and real datasets demonstrate the effectiveness of the SHOTUN method.
The method preserves intrinsic spatial consistency and offers an interpretable fusion process.

Conclusions:

SHOTUN provides an effective and interpretable solution for unsupervised hyperspectral and multispectral image fusion.
The proposed method overcomes limitations of conventional tensor decomposition and deep learning fusion approaches.
The pre-training strategy improves the model's generalization across diverse datasets and sensors.