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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. [ABSTRACT FROM AUTHOR]

The first seven bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used widely for global land-cover/land-use (LCLU) monitoring (e.g., deforestation over the Amazon basin). However, the spatial resolution of MODIS bands 3–7 (i.e., 500 m) is coarser than that of bands 1 and 2 (i.e., 250 m), and may be too coarse for a large number of applications. In this paper, a new geostatistical approach based on area-to-point regression kriging (ATPRK) is proposed for downscaling coarse spatial resolution bands 3–7 such as to produce a complete set of MODIS images at 250 m. ATPRK takes advantages of the fine spatial resolution information in bands 1 and 2 by regression modeling, and uses area-to-point kriging to downscale the coarse residuals from the regression. ATPRK was compared to four existing methods, including the principal component analysis, wavelets, high-pass filter and kriging with external drift (KED) methods for downscaling in two experiments on MODIS data from the Brazilian Amazon. Both visual and quantitative evaluations (in terms of the root mean square error, correlation coefficient, relative global-dimensional synthesis error, universal image quality index, spectral angle mapper and spectral information divergence) showed that ATPRK produced sharpened images with the greatest quality. In addition, ATPRK perfectly preserved the spectral properties of the original coarse data and was faster than KED. The results reveal the great potential of ATPRK applied to MODIS data for a wide variety of applications, including global monitoring of deforestation. The ATPRK proposed in this paper is an entirely new image fusion approach based on a new conceptualization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]