In this letter, we propose a simple yet robust procedure to combat the residual local misalignment between the image data sets that are usually processed for pansharpening: a higher resolution panchromatic (Pan) image and a series of lower resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. Unlike a conventional coregistration, which requires a preliminary orthorectification enforced by an accurate digital surface model, the proposed method automatically exploits the characteristics of the Pan image to alleviate the effects of misalignment on fusion products, whichever is the method chosen for pansharpening. More specifically, the space-varying residue of the multivariate regression between resampled MS bands and low-pass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignments, is injected into the MS bands after being weighted by the projection coefficients of each band. Tests on simulated Pléiades images demonstrate that global shifts up to five pixels along each direction are perfectly compensated. Tests on a true GeoEye-1 image, whose shifts are space-varying and of unknown extent, highlight the attained improvement in spatial alignment.
Aiazzi, B., Alparone, L., Garzelli, A., Santurri, L. (2018). Blind correction of local misalignments between multispectral and panchromatic images. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 15(10), 1625-1629 [10.1109/LGRS.2018.2850151].
Blind correction of local misalignments between multispectral and panchromatic images
Garzelli, Andrea;
2018-01-01
Abstract
In this letter, we propose a simple yet robust procedure to combat the residual local misalignment between the image data sets that are usually processed for pansharpening: a higher resolution panchromatic (Pan) image and a series of lower resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. Unlike a conventional coregistration, which requires a preliminary orthorectification enforced by an accurate digital surface model, the proposed method automatically exploits the characteristics of the Pan image to alleviate the effects of misalignment on fusion products, whichever is the method chosen for pansharpening. More specifically, the space-varying residue of the multivariate regression between resampled MS bands and low-pass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignments, is injected into the MS bands after being weighted by the projection coefficients of each band. Tests on simulated Pléiades images demonstrate that global shifts up to five pixels along each direction are perfectly compensated. Tests on a true GeoEye-1 image, whose shifts are space-varying and of unknown extent, highlight the attained improvement in spatial alignment.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1063096