A comprehensive investigation of image fusion methods for spatial enhancement of hyperspectral images


YILMAZ V.

INTERNATIONAL JOURNAL OF REMOTE SENSING, vol.43, no.11, pp.4151-4186, 2022 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 11
  • Publication Date: 2022
  • Doi Number: 10.1080/01431161.2022.2109223
  • Journal Name: INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.4151-4186
  • Keywords: Image fusion, hyperspectral imaging, remote sensing, image enhancement, INTENSITY MODULATION, MULTISPECTRAL IMAGES, TENSOR FACTORIZATION, CONTRAST, SUPERRESOLUTION, REGRESSION, PCA, MS

Abstract

The coarse spatial resolutions of hyperspectral (HS) satellite images limit their use in many applications. The spatial structure quality of HS images can be improved by fusing them either with higher-resolution panchromatic (PAN) images, or with higher-resolution multispectral (MS) images. Fusion of HS images can be done with fusion methods that are designed to fuse MS and PAN images, and the fusion methods developed for the fusion of HS and MS images. A wide variety of HS-MS and MS-PAN image fusion techniques can be used for the fusion of HS images, which leads the users to a hesitation as to which method(s) should be used for optimal fusion performance. Hence, the current study aimed to qualitatively and quantitatively assess the HS image fusion performances of a total of 15 MS-PAN image fusion methods and 17 state-of-the-art HS-MS image fusion techniques within four experiments, with the hope to give some clues on the performances of the fusion techniques used. Experiments showed that the HS-MS fusion methods exhibited much better HS image fusion performance, compared to the MS-PAN fusion methods used. It was also concluded that the coupled nonnegative matrix factorization (CNMF), convolutional neural network (CNN) denoiser-based method (CNN-D), HS super-resolution (HySure) and fast fusion based on Sylvester equation with naive Gaussian prior (FUSE-G) techniques provided the most robust fusion results.