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Image fusion in Remote Sensing (RS) has been a consistent demand due to itsability to turn raw images of different resolutions, sources, and modalitiesinto accurate, complete, and spatio-temporally coherent images. It greatlyfacilitates downstream applications such as pan-sharpening, change detection,land-cover classification, etc. Yet, image fusion solutions are highlydisparate to various remote sensing problems and thus are often narrowlydefined in existing reviews as topical applications, such as pan-sharpening,and spatial-temporal image fusion. Considering that image fusion can betheoretically applied to any gridded data through pixel-level operations, inthis paper, we expanded its scope by comprehensively surveying relevant workswith a simple taxonomy: 1) many-to-one image fusion; 2) many-to-many imagefusion. This simple taxonomy defines image fusion as a mapping problem thatturns either a single or a set of images into another single or set of images,depending on the desired coherence, e.g., spectral, spatial/resolutioncoherence, etc. We show that this simple taxonomy, despite the significantmodality difference it covers, can be presented by a conceptually easyframework. In addition, we provide a meta-analysis to review the major papersstudying the various types of image fusion and their applications over theyears (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally,we discuss the main benefits and emerging challenges to provide open researchdirections and potential future works.

Image Fusion in Remote Sensing: An Overview and Meta Analysis