Abstract: One of the important precondition of identification fusion based on remote sensing images is target association,which is to determine if the information from two or more images are related to the same target. A novel and robust point pattern matching method was presented for group target association in low-resolution remote sensing images.A new point set based invariant feature, Relative Shape Context (RSC) , was proposed. We used the test statistic of relafive shape context descriptor's matching scores as the foundation of mathematics model of group target association. For resolving the modcl,we firstly constructed the new compatibility measurement and used it to initialize the association probability matrix. Then the association probability matrix can be updated by relaxation labeling. The oncto-one matcking can be achieved by dual-normalization of rows and columns in the end. Experiments on both synthetic point sets and on real world data show that the group association algorithm is effective and robust.

Key words: Group target association, Point pattern matching, Relative shape context, Relaxation labeling