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 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。
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Analysis in one image level that has most pure objects will get high classification accuracy . the optimal segmentation scale means in that image level pure object is most and mix object is lest 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。
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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 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。
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The result is good for high - resolution images , but is inefficient for middle resolution images . developed another new method for selecting optimal segmentation scale : objects max - area method . the graph that shows the relationship of objects max - area and segmentation scale is ladder like increased 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。
