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Area-to-point regression kriging for pan-sharpening

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Area-to-point regression kriging for pan-sharpening. / Wang, Qunming; Shi, Wenzhong; Atkinson, Peter Michael.

In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114, 04.2016, p. 151-165.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wang, Q, Shi, W & Atkinson, PM 2016, 'Area-to-point regression kriging for pan-sharpening', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 151-165. https://doi.org/10.1016/j.isprsjprs.2016.02.006

APA

Wang, Q., Shi, W., & Atkinson, P. M. (2016). Area-to-point regression kriging for pan-sharpening. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 151-165. https://doi.org/10.1016/j.isprsjprs.2016.02.006

Vancouver

Wang Q, Shi W, Atkinson PM. Area-to-point regression kriging for pan-sharpening. ISPRS Journal of Photogrammetry and Remote Sensing. 2016 Apr;114:151-165. https://doi.org/10.1016/j.isprsjprs.2016.02.006

Author

Wang, Qunming ; Shi, Wenzhong ; Atkinson, Peter Michael. / Area-to-point regression kriging for pan-sharpening. In: ISPRS Journal of Photogrammetry and Remote Sensing. 2016 ; Vol. 114. pp. 151-165.

Bibtex

@article{bcbb9f5ddb5a429c9cbd975a0e09108f,
title = "Area-to-point regression kriging for pan-sharpening",
abstract = "Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.",
keywords = "Downscaling, Pan-sharpening, Geostatistics, Area-to-point regression kriging (ATPRK)",
author = "Qunming Wang and Wenzhong Shi and Atkinson, {Peter Michael}",
year = "2016",
month = apr,
doi = "10.1016/j.isprsjprs.2016.02.006",
language = "English",
volume = "114",
pages = "151--165",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Area-to-point regression kriging for pan-sharpening

AU - Wang, Qunming

AU - Shi, Wenzhong

AU - Atkinson, Peter Michael

PY - 2016/4

Y1 - 2016/4

N2 - Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

AB - Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

KW - Downscaling

KW - Pan-sharpening

KW - Geostatistics

KW - Area-to-point regression kriging (ATPRK)

U2 - 10.1016/j.isprsjprs.2016.02.006

DO - 10.1016/j.isprsjprs.2016.02.006

M3 - Journal article

VL - 114

SP - 151

EP - 165

JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

ER -