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

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.

• 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.