Considering that remote sensing images contain rich scale-dependent information and geographical detailed information, segmentation process must be carried out under the multiscale case. The vector-valued C-V active contour model is an effective image segmentation method, but the model cannot segment the nonhomogeneous remote sensing images well. The image processing methods based on nonsubsampled contourlet transform (NSCT) can fully use the detailed information of remote sensing images. The interscale distribution characteristics of NSCT coefficients at finer scale is first analyzed and then a statistical model of signal singularities combining the coefficient correlation between intrascale and interscale is proposed. Based on the above, the vector-valued C-V active contour model is then applied to the statistical characteristics for segmenting images. Consequently, the proposed method can preserve detailed information of images and other desirable properties of active contour model. Numerical examples indicate that the proposed method is very competitive with several state-of-the-art techniques.