中文摘要：

定义区域类的判别式空格是为在区域类的地图的无常描述的重要概念的构造。当他们能在在错误建模的区域类的印射和 replicability 提高一致性，判别式模特儿被提拔。作为区域班是很少在以经验为主地认识到的判别式空间完全可分离在班不可分离性为变化分类变得更复杂的地方，我们寻求确定在区域班上的无常(并且变化班) 由于测量错误和语义差异独立并且因此客观地估计他们的相对边缘。用真实数据集的实验被执行，并且一个贝叶斯的方法被用来获得变化地图。我们发现指数据类和信息类的无常统计之间有大差别。因此，在变化分类的无常描述应该基于判别式当模特儿测量错误和语义失配分析，分别地启用由于部分随机的测量错误，和系统的范畴的差异的无常的 quantification。

英文摘要：

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps. Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling. As area classes are rarely completely separable in empirically realized discriminant space, where class inseparability becomes more complicated for change categorization, we seek to quantify uncertainty in area classes （and change classes） due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively. Experiments using real datasets were carried out, and a Bayesian method was used to obtain change maps. We found that there are large differences between uncertainty statistics referring to data classes and information classes. Therefore, uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis, enabling quantification of uncertainty due to partially random measurement errors, and systematic categorical discrepancies, respectively.