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Object-Based Area-to-Point Regression Kriging for Pansharpening

Zhang, Yihang; Atkinson, Peter M.; Ling, Feng; Foody, Giles M.; Wang, Qunming; Ge, Yong; Li, Xiaodong; Du, Yun

Object-Based Area-to-Point Regression Kriging for Pansharpening Thumbnail


Authors

Yihang Zhang

Peter M. Atkinson

Feng Ling

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

Qunming Wang

Yong Ge

Xiaodong Li

Yun Du



Abstract

IEEE Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.

Citation

Zhang, Y., Atkinson, P. M., Ling, F., Foody, G. M., Wang, Q., Ge, Y., …Du, Y. (2021). Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 59(10), 8599-8614. https://doi.org/10.1109/TGRS.2020.3041724

Journal Article Type Article
Acceptance Date Nov 27, 2020
Online Publication Date Dec 14, 2020
Publication Date 2021-10
Deposit Date Dec 18, 2020
Publicly Available Date Jan 4, 2021
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 59
Issue 10
Pages 8599-8614
DOI https://doi.org/10.1109/TGRS.2020.3041724
Keywords Electrical and Electronic Engineering; General Earth and Planetary Sciences
Public URL https://nottingham-repository.worktribe.com/output/5157331
Publisher URL https://ieeexplore.ieee.org/document/9293157
Additional Information © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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