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An approach of multi-mutual information (M-MI) is presented for change detection and evaluation of building damages after an earthquake. Fusion of very high resolution pre-event optical and postevent synthetic aperture radar (SAR) images becomes feasible for timely evaluation of earthquake losses. Based on the geometric parameters extracted from an optical pre-event image, SAR images of rectangular building objects, i.e., nondamaged or damaged, are first numerically simulated by our mapping and projection approach and are then, using M-MI, applied to similarity analysis with the real postevent SAR image. Three models of building damages, i.e., collapsed, subsided, and deformed, are proposed for classifying mutual information (MI). The M-MI, including normalized MI (NMI), gradient MI (GMI), and regional MI (RMI), are all applied and compared for MI change detection of building damages. Based on the maximum, mean value, and height deviation of NMI, GMI, and RMI, the building damages can be detected and evaluated. As an example, the Ikonos pre-event and GeoEye postevent optical images and COSMO-SkyMed and Radarsat-2 postevent SAR images during the 2010 Haiti earthquake are applied in this M-MI experiment.