基于水平集理论的海岸线轮廓特征提取  

Feature extraction of coastline contour based on level set theory

作　　者：刘鹏程[1] 

机构地区：[1]绍兴文理学院元培学院,绍兴312000

出　　处：《国土资源遥感》2015年第2期75-79,共5页Remote Sensing for Land & Resources

基　　金：国家自然科学基金项目(编号:61272391);绍兴文理学院元培学院科研项目(编号:A20140004)共同资助

摘　　要：在处理分析海岸水陆特征时,引入水平集理论对复杂边界纹理特征信息进行提取。首先,对海岸线边界提取的相关研究及水平集理论进行了分析总结;然后,结合区域边界信息及其区域光滑非参数密度估计,引入海岸边界区域特征分割算法,利用多种类型的影像对该算法进行了验证;最后,为了说明水平集算法(level set method,LSM)对提取海岸线特征信息的有效性,对LSM算法与梯度下降方法在海岸线特征提取上的效率差异进行了比较。结果表明:LSM对海岸特征复杂纹理和噪声等信息具有一定的鲁棒性,同时对于有效边缘信息具有较强的检测灵敏度,能够迅速、有效地对其边界信息进行特征提取。This paper introduces level set theory to the feature extraction of coastline contour information. In this paper, the author first reviewed the related research work in this field and describes the level set theory and its applications, and then proposed the coastline contour segmentation algorithm and area smooth nonparametric density estimation before using it to extract different kinds of coastlines. To illustrate the effectiveness of the level set method （ LSM ） algorithm in coastline feature extracting, this paper compared the LSM algorithm and gradient descent method to demonstrate the coastline feature extraction efficiency of LSM. The optical and remote-sensing images used in experimental tests were of different contour features, multi-resolution and different point of views. The results achieved show that the level set algorithm is robust in analyzing characteristics of the coast complex texture even with the influence of noise. Also, it has strong sensitivity in edge information detection and is capable of quickly and effectively extracting features from the boundary information.

关 键 词：水平集理论 梯度下降法 海岸线 轮廓信息 特征提取 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]