Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality Decoupling

• URL: http://arxiv.org/abs/2412.04802v3
• Date: Tue, 22 Apr 2025 04:20:45 GMT
• Title: Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality Decoupling
• Authors: Songcheng Du, Yang Zou, Zixu Wang, Xingyuan Li, Ying Li, Changjing Shang, Qiang Shen,
• Abstract summary: Hyperspectral and Multispectral Image Fusion (HMIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs)<n>HMIF methods typically apply direct fusion from the two modalities without effective supervision.<n>We propose an end-to-end self-supervised textbfModality-Decoupled textbfSpatial-textbfSpectral Fusion (textbfMossFuse) framework.
• Score: 16.923899489263945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral and Multispectral Image Fusion (HMIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without effective supervision, leading to an incomplete perception of deep modality-complementary information and a limited understanding of inter-modality correlations. To address these issues, we propose a simple yet effective solution for unsupervised HMIF, revealing that modality decoupling is key to improving fusion performance. Specifically, we propose an end-to-end self-supervised \textbf{Mo}dality-Decoupled \textbf{S}patial-\textbf{S}pectral Fusion (\textbf{MossFuse}) framework that decouples shared and complementary information across modalities and aggregates a concise representation of both LR-HSIs and HR-MSIs to reduce modality redundancy. Also, we introduce the subspace clustering loss as a clear guide to decouple modality-shared features from modality-complementary ones. Systematic experiments over multiple datasets demonstrate that our simple and effective approach consistently outperforms the existing HMIF methods while requiring considerably fewer parameters with reduced inference time. The anonymous source code is in \href{https://github.com/dusongcheng/MossFuse}{MossFuse}.

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