River detection in high resolution remote sensing images is one of the most popular topics of research in computer vision. In this paper

a novel river detection algorithm in high-resolution remote sensing images by using support vector machine(SVM)and level set is proposed. According to the characteristic of the river

we employ texture feature and benchmark information diffusion feature as the feature vectors to train the support vector machine classifiers in order to perform the coarse segmentation of rivers. Then the distance regularized level set evolution(DRLSE) model

which takes the results of the coarse segmentation as the initial curves

is used to capture the desirable shapes of the rivers. Experiments are executed on IKONOS 1m-resolution images and the results demonstrate the superior performance of the proposed algorithm in terms of accuracy

efficiency

and robustness.