Automatic Fine Co-Registration of Datasets from Extremely High Resolution Satellite Multispectral Scanners by Means of Injection of Residues of Multivariate Regression

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors.