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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]

The point spread function (PSF) is ubiquitous in remote sensing. This paper investigated the effect of the PSF on the downscaling of continua. Geostatistical approaches were adopted to incorporate explicitly, and reduce the influence of, the PSF effect in downscaling. Two general cases were considered: univariate and multivariate. In the univariate case, the input coarse spatial resolution image is the only image available for downscaling. Area-to-point kriging was demonstrated to be a suitable solution in this case. For the multivariate case, a finer spatial resolution image (or images) observed under different conditions (e.g., at a different wavelength) is available as auxiliary data for downscaling. Area-to-point regression kriging was shown to be a suitable solution for this case. Moreover, a new solution was developed for estimating the PSF in image scale transformation. The experiments show that the PSF effect influences downscaling greatly and that downscaling can be enhanced obviously by considering the PSF effect through the geostatistical approaches and the PSF estimation solution proposed. [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]