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Information Loss-Guided Multi-Resolution Image Fusion

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Information Loss-Guided Multi-Resolution Image Fusion. / Wang, Q.; Shi, W.; Atkinson, P.M.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 58, No. 1, 31.01.2020, p. 45-57.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wang, Q, Shi, W & Atkinson, PM 2020, 'Information Loss-Guided Multi-Resolution Image Fusion', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 45-57. https://doi.org/10.1109/TGRS.2019.2930764

APA

Wang, Q., Shi, W., & Atkinson, P. M. (2020). Information Loss-Guided Multi-Resolution Image Fusion. IEEE Transactions on Geoscience and Remote Sensing, 58(1), 45-57. https://doi.org/10.1109/TGRS.2019.2930764

Vancouver

Wang Q, Shi W, Atkinson PM. Information Loss-Guided Multi-Resolution Image Fusion. IEEE Transactions on Geoscience and Remote Sensing. 2020 Jan 31;58(1):45-57. https://doi.org/10.1109/TGRS.2019.2930764

Author

Wang, Q. ; Shi, W. ; Atkinson, P.M. / Information Loss-Guided Multi-Resolution Image Fusion. In: IEEE Transactions on Geoscience and Remote Sensing. 2020 ; Vol. 58, No. 1. pp. 45-57.

Bibtex

@article{7d96022e3bcb416997de494bdf607384,
title = "Information Loss-Guided Multi-Resolution Image Fusion",
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.",
author = "Q. Wang and W. Shi and P.M. Atkinson",
year = "2020",
month = jan,
day = "31",
doi = "10.1109/TGRS.2019.2930764",
language = "English",
volume = "58",
pages = "45--57",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "1",

}

RIS

TY - JOUR

T1 - Information Loss-Guided Multi-Resolution Image Fusion

AU - Wang, Q.

AU - Shi, W.

AU - Atkinson, P.M.

PY - 2020/1/31

Y1 - 2020/1/31

N2 - 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.

AB - 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.

U2 - 10.1109/TGRS.2019.2930764

DO - 10.1109/TGRS.2019.2930764

M3 - Journal article

VL - 58

SP - 45

EP - 57

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 1

ER -