摘要
由于遥感图像存在边缘混叠等问题,经典的C-V模型会产生大量的冗余轮廓,而且无法分割多个同质区域的目标。为此,提出了基于C-V模型的窄带多区域水平集图像分割方法,采用N-1个水平集函数将图像分割成N(N>1)个区域,每个水平集函数表达一个区域。该方法一方面通过建立独立多区域水平集模型可以消除多余的轮廓,避免分割区域的重叠和漏分;另一方面应用窄带技术来减小水平集方法中的计算量。对遥感图像进行了实验,结果表明该方法能快速有效地分割该图像。
Massive redundant contours happen when the classical Chan-Vese（C-V） model is used to segment remote sensing images,which have interlaced edges.What＇s more,this model can＇t segment homogeneous objects with multiple regions.In order to overcome this limitation of C-V model,narrow band multiple level set method is proposed.The use of N-1 curves is required for the segmentation of N regions and each curve represents one region.First,the level set model to establish an independent multi-region region can eliminate the redundant contours and avoids the problems of vacuum and overlap.Then,narrow band approach to level set method can reduce the computational cost.Experimental results of remote image verify that our model is efficient and accurate.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2011年第11期3001-3005,共5页
Spectroscopy and Spectral Analysis
基金
科技部政府间国际科技合作项目(2009DFA12870)资助
关键词
图像分割
窄带水平集
多区域水平集
遥感图像
Image segmentation
Narrow band level set
Multi-region level set
Remote sensing image
