关键词: 核主元分析（KPCA）, 光谱特征, 测地线活动轮廓（GAC）, 基于距离正则化的水平集方法（DRLSE）, 水边线

Abstract: In order to make full use of multi-band spectral features of remote sensing images and improve the accuracy of extracted waterline, waterline extraction model from remote sensing data based on Kernel Principal Component Analysis（KPCA） and spectral features constrained is presented. First, the KPCA method is used to get the spectral features of water training samples, the maximum likelihood method is used to estimate the parameters of probability density function of water in feature space and then spectral feature term is constructed. Then image data term is constructed based on Geodesic Active Contour（GAC） model and combined with term spectral feature term and the image data term to establish waterline extraction model. At last, the experiments on Landsat TM datasets validate the effectiveness of the proposed model. Compared with Geodesic Active Contour（GAC） model and Distance Regularized Level Set Evolution（DRLSE） model, this model can improve the accuracy of extracted waterline in ensuring a certain speed of operation.

Key words: Kernel Principal Component Analysis（KPCA）, spectral feature, Geodesic Active Contour（GAC）, Distance Regularized Level Set Evolution（DRLSE）, waterline