An Adaptive Level Set Model with Feature Selection for Remote Sensing Image Segmentation

An adaptive level set model with feature selection for remote sensing image segmentation is proposed. The traditional C-V Model based on level set pays much attention to the color features, but with less emphasis on texture features. In the processing of remote sensing image, sometimes texture feature is more important for the purpose of image segmentation. To solve the problem, this paper firstly takes the components of different color spaces and the texture features as the initial feature set. Then feature selection is performed through local similarity analysis. Meanwhile, the weights of different features are adjusted accordingly. The selected features are utilized in the C-V model as inputs to segment the remote sensing image. Experimental results on various remote sensing imagery show that the newly proposed approach not only outperforms the traditional model efficiently, but also reduces the time cost greatly.