Coastline Detection in Synthetic Aperture Radar (SAR) Images by Integrating Watershed Transformation and Controllable Gradient Vector Flow (GVF) Snake Model

Abstract

Detection of coastline in synthetic aperture radars (SARs) is difficult due to the presence of speckle effect and strong signal return from wind-roughened, wave-modulated sea. This paper presents a new approach to detect coastlines from SAR images by integrating watershed transformation and gradient vector flow (GVF) snake model. Several improvements have been made to improve the accuracy and efficiency of coastline detection. First, ratio of averages edge detector is used to produce gradient maps suitable for watershed transformation. Second, an improved GVF snake model is presented, which exploits two external constraint forces to make the curve evolution more controllable. We name it controllable GVF (CGVF) snake model. Third, a coarse-fine processing scheme is employed, in which watershed transformation is performed on a coarse-resolution image to obtain the initial contours for CGVF snake model, and then CGVF snake model is used to refine the roughly detected coastline at fine resolution. Experimental results on Envisat-ASAR and TerraSAR-X images show that with only a modest computational burden, the new approach produces a good match between the detected coastline and the true one.