导　　师： 吴炳方

学科专业： G0503

授予学位： 博士

作　　者： ;

机构地区： 中国科学院遥感应用研究所

摘　　要： 该文在分析基于像元尺度问题研究方法的基础上,围绕面向对象影像分析中存在的尺度转换与最优尺度选择问题,开展了如下几个方面的研究:1.提出面向对象影像分析中最优尺度的双重性:一是提取面积最小类别所需的最大分辨率,二是不同类别提取的最优分割尺度.前者指在保证反映地物类别空间结构特征情况下识别最小地物类别的最大分辨率,是包含所需信息而且数据量最小的分辨率.后者是针对于一种地物类别而言的,影像对象多边形既不能太破碎,也不能边界模糊的分割尺度,该尺度下对象大小与特定的地物目标大小接近,且类别内部对象的光谱变异较小.2.提出以影像对象均值方差方法确定影像最优分割尺度.该方法能同时确定影像中多种地物类别的最优分割尺度,证明了不同类别有其相应的最优分割尺度,突破了最优尺度选择曲线只能适用于一个类别的限制,但该方法只适用于高分辨率影像信息提取最优尺度的选择,对于中低分辨率的影像效果不甚理想.3.提出以影像对象最大面积方法确定影像最优分割尺度.影像对象最大面积随分割尺度变化的曲线呈阶梯状上升的趋势,每一个曲线平台对应于每种类别提取适宜尺度的范围,该方法适用于不同分辨率影像的信息提取.通过曲线分析验证了最优尺度或适宜尺度是一个连续的范围值,而不是一个断点值的观点.4.提出三种不同的对象类型的概念:纯对象、混合对象与伪对象.影像信息提取在纯对象占多数的尺度层提取能保证有较高的分类精度,因此选择最优分割尺度也就是选择影像中纯对象最多而混合对象数目最少的分割尺度.5.以影像对象为基本实体的尺度转换方法就是影像的多尺度分割.不同尺度层上影像对象的属性信息是基于原始影像像元的整合,从而避免了重采样过程中损失大量的有用信息.6.提出可以 Entity, pattern and process show inherent hierarchy structure in nature. Scale issues correspond with hierarchy become one of important themes in many fields. The scale issues have increasingly attracted the attention of a number of scientists in nature and society sciences. It is widely acknowledged that it is a basic subject in remote sensing applications. Image information with different scale displays distinct spatial structure, so image analysis with single scale could not meet the heterogeneity and dynamic of pattern and process. Object-oriented image analysis creates meaningful objects and builds a hierarchy levels close to surface character using multi-scale segmentation. Different geography processes could be represented in corresponding image object levels. Object-oriented image analysis has realized multi-scale analysis of spatial pattern and process. In object-oriented image analysis 'scale' is not the pixel size, but the segmentation scale threshold. Scale is different for each remote sensing im age analysis. To show the variety of pattern and process correctly, it is important to understand image objects scale effect in scaling courses, and to select the optimal scale for special class abstraction. Based on methods and approaches in pixel-based image analysis, this thesis develops several results which focus on scaling and scale choose in object-oriented image analysis. Two aspects of optimal scale are exploited in object-oriented image analysis. One is the biggest resolution to extract the smallest class; the other is the optimal segmentation scale for different classes. Developed a new method for selecting optimal segmentation scale: mean variance method. From the graph many classes' optimal segmentation scales could be got. It proved that for different classes their optimal segmentation scale are different. It break the limitation of one optimal scale choose. There is one disadvantage for this method. The result is good for high-resolution images, but is in

关 键 词： 面向对象 多尺度分割 最优尺度 尺度转换 尺度效应 混合对象

领　　域： [自动化与计算机技术] [自动化与计算机技术]