Remote Sensing, Vol. 10, Pages 1969: Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery (Remote Sensing)

This paper describes an automatic multi-image robust alignment (MIRA) procedure able to simultaneously co-register a time series of medium-resolution satellite images in a bundle block adjustment (BBA) fashion. Instead of the direct co-registration of each image with respect to a reference ‘master’ image on the basis of corresponding features, MIRA also considers those tie points that may be not be shared with the master, but they only connect the other images (‘slaves’) among them. In a first stage, tie points are automatically extracted by using pairwise feature-based matching based on the SURF operator. In a second stage, such extracted features are re-ordered to find corresponding tie points visible on multiple image pairs. A ‘master’ image is then selected with the only purpose to establish the datum of the final image alignment and to instantiate the computation of approximate registration parameters. All the available information obtained so far is fed into a least-squares BBA to estimate the unknowns, which include the registration parameters and the coordinates of tie points re-projected in the ‘master’ image space. The analysis of inner and outer reliability of the observations is applied to assess whether the residual blunders may be located using data snooping, and to evaluate the influence of undetected outliers on the final registration results. Three experiments with simulated datasets and one example consisting of eleven Landsat-5/TM images are reported and discussed. In the case of real data, results have been positively checked against the ones obtained by using alternative procedures (BBA with manual measurements and ‘slave-to-master’ registration based on automatically extracted tie points). These experiments have confirmed the correctness of the MIRA approach and have highlighted the potential of the inner control on the final quality of the solution that may come from the reliability analysis.