From classical image fusion to deep representation learning: A survey of the advances in hyperspectral image pan-sharpening

Hyperspectral (HS) pan-sharpening has become a vital area of research, focusing on the fusion of HS and panchromatic (PAN) images to produce high-resolution hyperspectral (HRHS) data. Although traditional methods remain essential, the swift advancements in deep learning technologies require an updated analysis of how these approaches reshape the field. One of the major challenges in HS pan-sharpening is the limited availability of robust datasets, which often hinder the generalization of models across various sensors. This constraint complicates the development of universally applicable models. To address this gap, this survey comprehensively reviews traditional and deep learning-based methodologies, categorizing them into four primary groups based on their fusion techniques. This systematic categorization facilitates a thorough exploration of the underlying mechanisms and enables an assessment of the strengths and limitations of each approach. In addition, the survey highlights the emergence of hybrid models that combine traditional and deep learning methods, showing promise in improving performance and overcoming dataset constraints. Future research directions may focus on improving model generalization, investigating unsupervised and semi-supervised learning techniques, and advancing the accuracy and reliability of HS pan-sharpening models. Finally, we discuss emerging areas and provide insights on these future research directions to conclude this survey, aiming to stimulate further progress in this essential field of study.