Edge detection of riverway in remote sensing images based on curvelet transform and GVF snake

This paper introduces curvelet transform and gradient vector flow (GVF) snake to improvement accuracy in edge detection of waterway from remote sensing images. Multi-scale geometric analysis (MGA) is booming hot research topic in recent years, which aims to obtain flexible, fast and effective signal processing algorithms through efficient approximation and characterization for the inherent geometric structure of high-dimensional data. Curvelet transform is a special member of this emerging family of MGA which overcomes inherent limitation of traditional multi-scale representation such as wavelet which ignores the geometric properties of objects with edges and does not exploit the regularity of the edge curves in higher dimension. The basic edge detection process is mainly composed of three parts. Firstly, obtain the initial snake based on region growing and morphology methods from curvelet-based dedoised image. Secondly, get the edge map derived from curvelet-based enhancement image. Finally, obtain the converging snake by evolving the GVF snake. The edge detection results of Yangtze River derived from the proposed method, wavelet based GVF snake and canny method are compared together. Experiments demonstrate that the new algorithm is superior to other methods, which is more effective and accurate.