A Comprehensive Overview of Satellite Image Fusion: From Classical Model-Based to Cutting-Edge Deep Learning Approaches

Earth observation satellites usually acquire a high-resolution image with a very limited number of spectral bands along with a lower-resolution image that accurately encodes the spectral responses of objects in the scene. Satellite image fusion, also known as pansharpening or hypersharpening depending on the characteristics of the data, aims to combine the spatial and spectral information into a single high-resolution multispectral or hyperspectral image. The resulting image is then used in a wide variety of remote sensing applications, for which low-ground sampling distance and detailed description of the chemical-physical composition of the objects may be required. In this chapter, we review the state of the art of satellite image fusion, emphasizing the crucial role that the modelling of the problem plays in performance and analysing how deep learning has changed the paradigm. This study enables comprehending the evolution of various approaches and their respective outcomes. We establish a fair comparison process, standardizing a general strategy to train and evaluate fusion methods. Quantitative and qualitative comparisons are conducted on several datasets with distinct resolutions and sensor characteristics. The source codes ( https://github.com/TAMI-UIB/COFI ) used for the comparison are published freely for non-commercial use.