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This letter presents a novel coarse-to-fine level set method for contour extraction in optical satellite images. To distinguish objects from a background, the undecimated wavelet transform is firstly adopted to extract image features, and a homogeneity metric is defined to measure the variation of the features inside and outside contours. In addition, the weight distribution ratio is proposed to adaptively tune the relative weight of the features. Based on the homogeneity metric and the weight distribution ratio, a novel energy functional is developed to model a contour extraction problem, and in order to reduce the computation burden, a coarse-to-fine scheme is applied to progressively extract contours in finer scale, during which a contour position constraint is introduced to limit contours evolving in a small space around the candidate contours extracted in coarser scale. Extensive experiments have been carried out on optical satellite images to validate the proposed method.