Shoreline detection plays an important role in vision based navigation for autonomous surface vehicles (ASVs). It is a challenging task because of the diversity in near-bank scenarios. In this paper, we present a two-stage algorithm to find the shoreline by employing multiple features. First, we classify images into two types: reflection-unidentifiable and reflection-identifiable. Based on this classification, images are further analyzed with suitable techniques respectively. In the reflection-unidentifiable case, the surface reflection is subtle and so the water region can be separated from land by an adaptive thresholding method. The points along the edge of the water region are then identified and the shoreline is estimated through a line-fitting technique. In the reflection-identifiable case, we aim to discriminate water regions from the land by means of a two-category region classifier. Images are oversegmented into small regions based on color homogeneity. Then the characteristic features of land-water scenes like symmetry and brightness are extracted and applied to classify a region into land or water categories. Experimental results show the efficacy of our approach and robustness in diverse situations.