计算机工程与应用2018，Vol.54Issue(11)：171-177,7.DOI:10.3778/j.issn.1002-8331.1701-0030
基于KPCA光谱特征约束的水边线提取算法
Waterline extraction algorithm based on KPCA and spectral features constrained
陈飞宇 1阮鲲 2胡友彬 1曹磊3
作者信息
- 1. 解放军理工大学 气象海洋学院,南京211101
- 2. 南京师范大学 地理科学学院,南京211101
- 3. 江苏华高软件技术有限公司 大气海洋部,南京211101
- 折叠
摘要
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 Geo-desic 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.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Key words
Kernel Principal Component Analysis(KPCA)/spectral feature/Geodesic Active Contour(GAC)/Distance Regularized Level Set Evolution(DRLSE)/waterline分类
信息技术与安全科学引用本文复制引用
陈飞宇,阮鲲,胡友彬,曹磊..基于KPCA光谱特征约束的水边线提取算法[J].计算机工程与应用,2018,54(11):171-177,7.
