Abstract: Abstract: With the development of the remote sensing satellite technology, high-resolution remote sensing images are on an increasing trend. The automatic target extraction from remote sensing images containing other information and complex background urgently needs to be realized. The traditional image segmentation method mainly depended on such underlying features as image spectrum and texture, and in image segmentation tasks, was likely to be impacted by the interference of shadow and occlusion in the image, complicating the segmentation and leading to unsatisfactory results. For this reason, according to the specific target type, a CV (Chan Vest) image segmentation model combined with local and global features of the target was proposed. Firstly, the deep learning generation model-CRBM (convolution restricted Boltzmann machine) was employed to represent the global shape features of the target and to reconstruct the shape of the target. Secondly, the edge information of the target was extracted by Canny operator, and a new shape constraint term integrating the local edge and global shape information was obtained by symbolic distance transformation. Finally, the CV model served as the image target segmentation model, and new constraints were added to gain the CV remote sensing image segmentation model integrating the local and global features of the target. The experimental results on the remote sensing dataset Levir-oil drum, Levir-ship and Levir-airplane show that the proposed model can not only overcome the noise sensitivity of the CV model, but also segment the target completely and accurately in the case of limited training data, small target size, occlusion and complex background.

Key words: Keywords: image segmentation, shape prior, convolutional restricted Boltzmann machine, deep learning, Chan Vest model