面向对象影像分类中分割参数的选择  

Automatic Selection of Segmentation Parameters for Object Oriented Image Classification

作　　者：薄树奎[1] 韩新超[2] 丁琳[3] 

机构地区：[1]郑州航空工业管理学院计算机科学与应用系,郑州市大学中路2号450015 [2]中原工学院计算机科学与技术系,郑州市中原中路41号450007 [3]中国科学院遥感应用研究所,北京市大屯路甲20号100101

出　　处：《武汉大学学报（信息科学版）》2009年第5期514-517,共4页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(40771140);国家973计划资助项目(2007CB714406)

摘　　要：提出了一种基于区域生长方法的分割参数选择方案,从各个类别的训练样区中提取分割参数信息。通过一系列的影像区域分割,计算得出一个最大的目标函数值,为每个类别推演出最佳分割参数;在单个类别参数影像分割和分类的基础上,融合所有处理结果,最后完成影像分类。实验验证了所提出方法的有效性。Image segmentation is prerequisite for object-oriented image analysis. Most image segmentation algorithms need the user to provide parameters to control the quality of the resulting segmentation. Selecting suitable parameters is a challenging task in using such algo- rithms. We proposed a method of parameters selection for region-growing image segmentation. Information about segmentation parameters was extracted from training sample areas of each class in the image. By multiple-segmentation of the training sample area, a maximum of objective function was found to deduce the suitable parameters for a class. Using the obtained parameters, n（the number of classes） resulting segmentations and subsequent resulting classifications were achieved. Then the n resulting classifications were fused to complete the final image classification. We tested the parameters selection for image segmentation in an object-oriented classification of remote sensing image.

关 键 词：参数选择 分割 分类 面向对象 遥感影像 

分 类 号：P237.4[天文地球—摄影测量与遥感] TP753[天文地球—测绘科学与技术]