面向对象遥感影像分类的最优分割尺度选择研究

Optimal Segmentation Scale Selection for Object-oriented Remote Sensing Image Classification

影像分割是面向对象遥感影像分类的基础步骤,而分割尺度又是影像分割的核心问题。研究针对面向对象遥感影像分类中的最优分割尺度选择问题,以分割后影像区域对象矢量边界线与欲分类目标对象真实矢量边界的吻合程度为标准,通过两者多向距离量化吻合程度,提出了一种最优分割尺度定量选择的新方法——矢量距离指数法。通过两种实验,同步验证了该方法的正确性与适用性,实验1将基于矢量距离指数法选择的最优分割尺度结果与较为成熟的人为试错法的选择结果比较,结果表明针对7种地类的矢量距离指数均可以正确反映最优分割尺度;实验2挖掘了矢量距离指数法选择的结果与分类精度的关系,结果表明其中5种地类在矢量距离指数法选择的最优分割尺度上均达到了最大的分类精度,另外2种地类的分类结果最符合实地情况,与欲分类目标最为接近。基于矢量距离指数法基本原理,研究针对分割过程中的"淹没"与"破碎"现象,进一步提出了能够反映两者矛盾程度的尺度指数,该指数能够真实反映针对某种特定地物类型分割尺度的大小状况,为衡量"破碎"与"淹没"的矛盾程度提供了一种定量工具,在分割尺度选择过程中具有重要的指示意义。

Segmentation is a basic and pivotal step of object-oriented remote sensing image classification, and the scale is a key problem of image segmentation. Aiming at the optimal segmentation scale selection for object-oriented remote sensing image classification, conformity degree between vectorial boundary lines of image region object after segmentation and true boundary lines of classification objects as criterion, through their multi-dimensions distance to define the conformity degree, the paper brought forward a new method of optimal segmentation scale selection for object-oriented remote sensing image classification-vector distance index method. Research verified the validity and applicability of this method through two experiments. One experiment compared the results of optimal segmentation scale selection based on vector distance index method and ＇ trial and error＇ method. Results showed the vector distance index could reflect the optimal segmentation scale for seven classes exactly. The other experiment classified the segmentation result from the first experiment, through the accuracy assessment, explored the relationship of selection result based on vector distance index method and classification accuracy. Results showed water, dryland, rice, forest and resident region gained the highest accuracy on the scale that was selected by the vector distance index method, although marsh and grass didn＇t gain the highest accuracy on the scale that was selected by the vector distance index method, through further analysis, the classification result tallied with the practical condition. Then both two experiment results showed that this method could realize the optimal segmentation scale selection for object-oriented remote sensing image classification, which was similar to human thought, unconfined to data source, intuitionistic, comprehensible and practical. Based on the basic theory of vector distance index method, aiming at the ＇ submergence＇ and ＇ fragmentation＇ phenomenon, research brought forward a scale index, which could reflect the small or big status for a given object type, and provided a quantitative tool to assess the conflict degree between ＇ submergence＇ and ＇ fragmentation＇ , then showed its significance during the process of segmentation scale selection.