This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas Sahil Suri and Peter Reinartz Abstract—The launch of high-resolution remote sensing satel- lites like TerraSAR-X, WorldView, and Ikonos has benefited the combined application of synthetic aperture radar (SAR) and optical imageries tremendously. Specifically, in case of natural calamities or disasters, decision makers can now easily use an old archived optical with a newly acquired (postdisaster) SAR image. Although the latest satellites provide the end user already georeferenced and orthorectified data products, still, registration differences exist between different data sets. These differences need to be taken care of through quick automated registra- tion techniques before using the images in different applications. Specifically, mutual information (MI) has been utilized for the intricate SAR–optical registration problem. The computation of this metric involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g., SAR and opti- cal). Satellites carrying high-resolution remote sensing sensors like TerraSAR-X and Ikonos generate enormous data volume along with fine Earth observation details that might lead to failure of MI to detect correct registration parameters. In this paper, a solely histogram-based method to achieve automatic registration within TerraSAR-X and Ikonos images acquired specifically over urban areas is analyzed. Taking future sensors into a perspective, tech- niques like compression and segmentation for handling the enor- mous data volume and incompatible radiometry generated due to different SAR–optical image acquisition characteristics have been rightfully analyzed. The findings indicate that the proposed method is successful in estimating large global shifts followed by a fine refinement of registration parameters for high-resolution images acquired over dense urban areas. Index Terms—High resolution, image matching, remote sensing. I. I NTRODUCTION D URING the last decades, remote sensing sensors have undergone a rapid development in terms of both data quantity and characteristics. Recently, there has been a signifi- cant increase in the number of high-resolution sensors placed in remote sensing satellites like Ikonos, Quickbird, TerraSAR-X, Cosmo-Skymed, and WorldView-1. With this enormous in- crease in availability and quality of remote sensing data prod- Manuscript received February 26, 2009; revised May 8, 2009. S. Suri is with the Remote Sensing Technology Institute, German Aerospace Centre (DLR), 82234 Wessling, Germany, and also with the Technical Univer- sity of Munich, 80333 Munich, Germany (e-mail: sahil.suri@dlr.de). P. Reinartz is with the Remote Sensing Technology Institute, German Aerospace Centre (DLR), 82234 Wessling, Germany (e-mail: peter.reinartz@ dlr.de). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2009.2034842 ucts, remote sensing imagery and techniques have found ap- plications in diverse areas like traffic studies, treaty and bor- der monitoring, agricultural studies, generation of 3-D models and topographic maps, early-warning systems, urban growth monitoring, damage assessment, and, specifically, disaster mit- igation. In general, images acquired both by the passive opti- cal sensors and active synthetic aperture radar (SAR) sensors (alone and in combination) are major sources for crisis informa- tion management. In particular, the SAR sensor’s active nature using microwaves gives them the capability to see through clouds and to acquire images at night, which might be the only possible option during a catastrophic event. However, images acquired by SAR sensors have very different characteristics from normally used optical sensor images. On top of the very different geometry (sideways looking and measuring distances) from their optical counterparts (measuring angles), images ac- quired by SAR sensors show a high amount of speckle influence caused by coherent source of imaging. Normally, remote sens- ing applications might need to accommodate images from dif- ferent sensors/modalities, depending upon specific application demands or data unavailability. For example, in case of a natural calamity, decision makers might be forced to use old archived optical data with a newly acquired (postdisaster) SAR image. Combined application of data from different sensors requires georeferenced and fine coregistered images for an accurate and successful analysis. Although latest satellites provide the end user already georeferenced and orthorectified data products, still, registration differences exist between various data sets acquired from different sources even after a digital elevation model (DEM)-supported orthorectification process. These dif- ferences need to be taken care of through quick automated registration techniques before using the images in different applications. Image registration refers to the task of aligning two or more images acquired at different times, from different sensors or from different view points. Image registration can roughly be classified into categories, namely, feature- and intensity-based techniques. Feature-based techniques depend upon detecting and matching landmark features within the images, and on the other hand, in intensity-based techniques, images are registered based on a relation between pixel intensity values of two images. An extensive overview and survey of various image registration methods used in the aforementioned fields can be found in the literature [1], [2]. Recent developments in remote sensing image registration are available in [3] and [4]. The complexity of registering SAR and optical data from high-resolution sensors particularly in urban areas can be 0196-2892/$26.00 © 2009 IEEE Authorized licensed use limited to: Deutsches Zentrum fuer Luft- und Raumfahrt. Downloaded on January 11, 2010 at 06:16 from IEEE Xplore. Restrictions apply.