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  • Object-based Area-to-point Regression Kriging for Pansharpening

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

Research output: Contribution to journalJournal articlepeer-review

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Object-Based Area-to-Point Regression Kriging for Pansharpening. / Zhang, Y.; Atkinson, P.M.; Ling, F.; Foody, G.M.; Wang, Q.; Ge, Y.; Li, X.; Du, Y.

In: IEEE Transactions on Geoscience and Remote Sensing, 14.12.2020.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Zhang, Y, Atkinson, PM, Ling, F, Foody, GM, Wang, Q, Ge, Y, Li, X & Du, Y 2020, 'Object-Based Area-to-Point Regression Kriging for Pansharpening', IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.3041724

APA

Zhang, Y., Atkinson, P. M., Ling, F., Foody, G. M., Wang, Q., Ge, Y., Li, X., & Du, Y. (2020). Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.3041724

Vancouver

Zhang Y, Atkinson PM, Ling F, Foody GM, Wang Q, Ge Y et al. Object-Based Area-to-Point Regression Kriging for Pansharpening. IEEE Transactions on Geoscience and Remote Sensing. 2020 Dec 14. https://doi.org/10.1109/TGRS.2020.3041724

Author

Zhang, Y. ; Atkinson, P.M. ; Ling, F. ; Foody, G.M. ; Wang, Q. ; Ge, Y. ; Li, X. ; Du, Y. / Object-Based Area-to-Point Regression Kriging for Pansharpening. In: IEEE Transactions on Geoscience and Remote Sensing. 2020.

Bibtex

@article{bc7f1d98a4f64dd685915d62f06dfd1e,
title = "Object-Based Area-to-Point Regression Kriging for Pansharpening",
abstract = "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. IEEE",
keywords = "Bandwidth, Downscaling, geostatistics, image fusion, Image segmentation, Image sensors, object-based, Optical sensors, pansharpening, Satellites, segmentation., Sensors, Spatial resolution, Aggregates, Image resolution, Interpolation, Pixels, Semantics, Comprehensive assessment, Earth observation satellites, Multispectral images, Panchromatic (Pan) image, Quantitative assessments, Satellite sensor images, Semantic information, Spectral information",
author = "Y. Zhang and P.M. Atkinson and F. Ling and G.M. Foody and Q. Wang and Y. Ge and X. Li and Y. Du",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/TGRS.2020.3041724",
language = "English",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - Object-Based Area-to-Point Regression Kriging for Pansharpening

AU - Zhang, Y.

AU - Atkinson, P.M.

AU - Ling, F.

AU - Foody, G.M.

AU - Wang, Q.

AU - Ge, Y.

AU - Li, X.

AU - Du, Y.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2020/12/14

Y1 - 2020/12/14

N2 - 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. IEEE

AB - 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. IEEE

KW - Bandwidth

KW - Downscaling

KW - geostatistics

KW - image fusion

KW - Image segmentation

KW - Image sensors

KW - object-based

KW - Optical sensors

KW - pansharpening

KW - Satellites

KW - segmentation.

KW - Sensors

KW - Spatial resolution

KW - Aggregates

KW - Image resolution

KW - Interpolation

KW - Pixels

KW - Semantics

KW - Comprehensive assessment

KW - Earth observation satellites

KW - Multispectral images

KW - Panchromatic (Pan) image

KW - Quantitative assessments

KW - Satellite sensor images

KW - Semantic information

KW - Spectral information

U2 - 10.1109/TGRS.2020.3041724

DO - 10.1109/TGRS.2020.3041724

M3 - Journal article

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

ER -