{
  "id": "2401.08837",
  "version": "v1",
  "published": "2024-01-16T21:21:17.000Z",
  "updated": "2024-01-16T21:21:17.000Z",
  "title": "Image Fusion in Remote Sensing: An Overview and Meta Analysis",
  "authors": [
    "Hessah Albanwan",
    "Rongjun Qin",
    "Yang Tang"
  ],
  "comment": "21pages, 10 figures",
  "categories": [
    "cs.CV",
    "eess.IV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-01-16T21:21:17.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "remote sensing",
      "meta analysis",
      "simple taxonomy defines image fusion",
      "spatial-temporal image fusion",
      "greatly facilitates downstream applications"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 21,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}