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ARRSI: Automatic registration of remote-sensing images

TitleARRSI: Automatic registration of remote-sensing images
Publication TypeJournal Article
Year of Publication2007
AuthorsWong, A., and D. A. Clausi
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume45
Pagination1483 - 1493
ISSN0196-2892
KeywordsARRSI, Automatic Registration of Remote-Sensing Images, contrast issue, control point detection, image registration, intensity-difference issue, matching process, maximum distance sample consensus, Multimodal registration, performance evaluation, random sample and consensus algorithm, Remote Sensing, squared differences registration techniques
Abstract

This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested

DOI10.1109/TGRS.2007.892601