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One of the most important steps in data fusion is image registration. Automatic image-to-image registration for images captured by different sensors traditionally requires the use of information-theoretic similarity measures such as mutual information. Recently, a new similarity measure known as cross-cumulative residual entropy (CCRE) has been proposed for multimodal image registration in medical imaging applications. In this paper, we investigate the use of CCRE for multisensor registration of remote sensing imagery. In particular, we investigate the extreme case of registering synthetic aperture radar images to optical images. We also propose a novel extension to the Parzen-window optimization approach proposed by Thévenaz which involves applying partial volume interpolation in the calculation of the gradients of the similarity measure. Our experimental results show that our proposed approach which uses CCRE as the similarity measure and partial volume interpolation in the optimization procedure provides superior performance to other approaches investigated.