中文摘要：

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。

英文摘要：

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.