Efficient Globally Optimal Registration of Remote Sensing Imagery via Quasi-Random Scale-Space Structural Correlation Energy Functional
A novel energy functional for automatic registration of remote sensing imagery based on quasi-random scale-space structural correlation is presented. The structural correlation energy functional takes advantage of the fact that, for many types of remote sensing imagery, there exist common structures at different scales even if the acquired images have very different intensity characteristics. The proposed energy functional also takes advantage of the noise robustness and feature localization properties of quasi-random scale-space theory. An efficient globally exhaustive optimization strategy in the frequency domain is developed for registering remote sensing imagery based on the proposed energy functional. Promising test results on interband, intraband, and intermodal remote sensing image sets show that the proposed method has the advantage of being robust to differing sensing conditions and large misalignments.