基于空间数据的表示不仅要依赖于尺度特征的研究，而且只有在特定的尺度下提取的要素特征才具有现实意义。由于不同尺寸的结构元素适合不同空间尺度的图像特征。结构元素尺寸过大或者过小都会达不到最佳的特征边缘提取效果。因此结构元素尺寸的设计在提取图像特征信息中起着重要的作用。通过数学形态学梯度性质与结构元素尺寸关系的分析，首先得出了结构元素尺寸占与特征要素边缘尺寸γ和特征要素空间尺度λ之间的定量关系，然后通过计算比较相邻尺度间梯度图像的差值信息设计出了一种最佳结构元素尺寸的自适应确定算法。试验结果表明，该算法能够根据特征要素边缘的尺寸来求出最佳地结构元素尺寸。通过典型梯度算子提取的边缘与利用该算法设计的结构元素尺寸提取的边缘的比较结果证明，该算法在稳健性、定位精度和对比度等方面都优于典型梯度算子。

Because the representation of the spatial data is related with scale characteristics, element feature extraction is meaningful only when it is under certain scale rcpectively. Different sizes of structure element are suitable for image features on different spatial scales. It cannot obtain the optimal effects can not be obtained if the size of structure element. Therefore, the design of size of structure element plays an important role on extracting image information. Based on the analysis between the mathematical morphology characteristic and the size of structure element, the optimal quantitivc relationship is acquired, between the size of structure element, the size of feature factor and special scale of feature factor denotes size of structure element is unsuitable, A denotes size of feature factor and denotes spatial scale of feature factor. Then the adaptive algorithm for ascertaining the optimal size of structure element is designed by calculating and comparing the difference information between the neighborhood spatio-scale gradient images. And show experimental results the correctness and effectiveness of the algorithm. In the end, the difference between the extraction result by the classical edge operators and the operator presented in the paper are listed and analyzed,then the conclusion that the operator is superior to other edge operators on robust, position and contrast is reached.