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.
Highlights
D OWNSCALING is a process to increase the spatial resolutions of observations [1]
Let ZCl (xi ) be the measurements of pixel C centered at xi (i = 1, . . . , M, where M is the number of pixels) in coarse band l (l = 1, . . . , L, where L is the number of coarse bands), and ZFk(x j ) be the measurements of pixel F centered at x j [ j = 1, . . . , M G2, where G is the spatial resolution ratio] in fine band k (k = 1, . . . , K, where K is the number of fine bands)
area-to-point kriging (ATPK) is employed for initial downscaling, while geographically weighted regression (GWR) transforms the information loss (IL) from the fine bands covering the same area, but in other wavelengths, to that for the coarse band
Summary
D OWNSCALING is a process to increase the spatial resolutions of observations [1]. For remote sensing images, such a process involves the change-of-support problem (COSP), where the support is a geostatistical term meaning the space on which an observation or measurement is defined. The geostatistics-based area-to-point kriging (ATPK) technique is an effective solution to the COSP, which can predict support that is smaller than that of the original data [2], [3]. ATPK was originally developed for census data (e.g., disease or health data) involving irregular geographical units (e.g., county) with different sizes and shapes [4]. The technique was popularized and extended to the remote sensing case which.
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