Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43699
Title: Area-to-point regression kriging for pan-sharpening
Authors: Wang, Q
Shi, W 
Atkinson, PM
Keywords: Area-to-point regression kriging (ATPRK)
Downscaling
Geostatistics
Pan-sharpening
Issue Date: 2016
Publisher: Elsevier
Source: ISPRS journal of photogrammetry and remote sensing, 2016, v. 114, p. 151-165 How to cite?
Journal: ISPRS journal of photogrammetry and remote sensing 
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.
URI: http://hdl.handle.net/10397/43699
ISSN: 0924-2716
DOI: 10.1016/j.isprsjprs.2016.02.006
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