%0 Journal Article
%T 基于岸线特征点合并的极化SAR图像小型港口检测<br>Small harbor detection in polarimetric SAR images based on coastline feature point merging
%A 刘春
%A 殷君君
%A 杨健
%J 清华大学学报（自然科学版）
%D 2015
%X 为了对极化合成孔径雷达(polSAR)图像中小型港口目标进行自动检测, 在分析小型港口特性的基础上, 提出了一种基于岸线特征点合并的检测方法。首先, 使用极化SAR图像水平集分割算法实现精确的海岸线提取, 并通过数字曲线分裂归并算法提取海岸线轮廓特征点; 然后针对小型港口轮廓特征点比非港口区域轮廓的密集的特性, 提出了一种岸线特征点合并算法实现港口检测。分别用RADARSAT-2系统获取的新加坡和湛江海岸区域极化SAR数据对提出方法进行了试验。实验结果表明, 该方法能够正确地检测沿岸小型港口。<br>Abstract：A method was developed to automatic detect small harbors in polarimetric synthetic aperture radar (polSAR) images using coastline feature point merging based on analyses of structural characteristics of harbors. The coastline is accurately extracted by level set segmentation algorithm of polSAR with the coastline feature points then detected with a split and merge algorithm for digital curves. Then, the algorithm takes advantage of the characteristic that feature points along small harbor contour are denser than those along other coastline contours using a merging algorithm to detect the small harbors. The detection scheme was tested using polarimetric SAR images acquired by RADARSAT-2 over Singapore and the Zhanjiang area of China. The results show that almost all the harbors along the coastline are correctly detected by this method.
%K 合成孔径雷达
%K 港口检测
%K 极化
%K 水平集分割
%K 特征点合并
%K &lt
%K br&gt
%K synthetic aperture radar
%K harbor detection
%K polarization
%K level set segmentation
%K point merge
%U http://jst.tsinghuajournals.com/CN/Y2015/V55/I8/849#FigureTableTab