In order to improve the accuracy of water area segmentation in high resolution remote sensing image, the image entropy was introduced into CV model because there was a quite difference of texture complexities between water area and background, and two active counter models based on image entropy were proposed in this paper. The image entropy of inside zero level set was adopted in CV model and forms a local image entropy active counter model (LIEACM). This model effectively reduced the incorrect segmentation of background where the gray value approximated to the water area with low texture complexity. For remote sensing image of water area with high texture complexity, the global image entropy active counter model (GIEACM) was proposed, in which, the image entropy of inside and outside of zero level set were employed in CV model simultaneously. GLEACM modifies the fact that the level set function evolution depends on gray value, and the zero level set cald evaluate to the global optimal value. The experiments on segmentation the lake, river and sea validate that the segmentation precisions of LIFACM are 90.1%, 81.5% and 93.6%, respectively, the F-measures are 0.94, 0.885 and 0.96, respectively; and for GLEACM, the segmentation precisions are 94.5%, 853% and 94.9%, respectively, the F-measures are 0.956, 0.895 and 0.967, respectively. The two image entropy active contour models proposed by this paper improve the water area segmentation accuracy in remote sensing image effectively.