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Precise image-to-image registration is required to use multi-sensor data implementing a diversity of applications related with remote sensing. The purpose of this paper is to develop an automatic algorithm that co-registers high-resolution optical and SAR images based on an integrated intensity-and feature-based approach. As a pre-registration step, initial differences between the translation of the x and y directions between images were estimated with the Simulated Annealing optimization method using Mutual Information as an objective function. After the pre-registration, the line features were extracted to design a cost function that finds matching features based on the similarities of their locations and gradient orientations. Only one feature at each regular grid region having a minimum value of cost function was selected as a final matching point to extract the large number of well-distributed points. The final points were then used to construct a transformation combining the piecewise linear function with the affine transformation to increase the accuracy of the geometric correction.