Information Loss-Guided Multi-Resolution Image Fusion

Qunming Wang, Wenzhong Shi, Peter M. Atkinson

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods.

Original languageEnglish
Article number8842599
Pages (from-to)45-57
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume58
Issue number1
DOIs
Publication statusPublished - Jan 2020

Keywords

  • Downscaling
  • geographically weighted regression (GWR)
  • geostatistics
  • image fusion
  • information loss (IL)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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