Image Fusion in Remote Sensing: An Overview and Meta Analysis
Hessah Albanwan Rongjun Qin Yang Tang
Image fusion in Remote Sensing (RS) has been a consistent demand due to its
ability to turn raw images of different resolutions, sources, and modalities
into accurate, complete, and spatio-temporally coherent images. It greatly
facilitates downstream applications such as pan-sharpening, change detection,
land-cover classification, etc. Yet, image fusion solutions are highly
disparate to various remote sensing problems and thus are often narrowly
defined in existing reviews as topical applications, such as pan-sharpening,
and spatial-temporal image fusion. Considering that image fusion can be
theoretically applied to any gridded data through pixel-level operations, in
this paper, we expanded its scope by comprehensively surveying relevant works
with a simple taxonomy: 1) many-to-one image fusion; 2) many-to-many image
fusion. This simple taxonomy defines image fusion as a mapping problem that
turns either a single or a set of images into another single or set of images,
depending on the desired coherence, e.g., spectral, spatial/resolution
coherence, etc. We show that this simple taxonomy, despite the significant
modality difference it covers, can be presented by a conceptually easy
framework. In addition, we provide a meta-analysis to review the major papers
studying the various types of image fusion and their applications over the
years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally,
we discuss the main benefits and emerging challenges to provide open research
directions and potential future works.