摘要
将云模型与模糊理论相结合,提出了一种基于不确定性的、顾及几何特征的遥感影像分割方法。该方法用分水岭算法对原影像进行分割获得初始分割图斑,再将图斑抽象成云模型,以云模型实现图斑对象表达;在基于云概念的光滑度、紧凑度定义的基础上,构造差异性度量准则和相应的云模型综合算法,并由云综合运算实现不同粒度空间下的区域合并,达到多尺度遥感影像分割的目的。几组实际影像数据的分割实验证明了该方法的有效性。
Cloud model, an information transition model between qualitative and quantitative, is beneficial to the processing of uncertainty in remote sensing image. Aiming to deal with uncertainty better, a new approach for image segmentation which integrates cloud model and fuzzy theory is proposed. This approach adopts watershed algorithm to produce clumps from original image. These original clumps are abstracted to cloud models and described with cloud models parameters. The cloud integration algorithm is constructed via the differential measurement rule which is defined by the cloud notion. And cloud models, similar in color, shape and topology, are merged together by integration of cloud notion. Thus, the clumps are combined in different granularity spaces and the multi-scale remote sensing image segmentation is realized.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2008年第9期939-942,共4页
Geomatics and Information Science of Wuhan University
基金
国家863计划资助项目(2007AA12Z143)
国家自然科学基金资助项目(40201039
40771157)
关键词
云模型
遥感影像分割
几何特征
cloud model
remote sensing image segmentation
geometrical features
