Hyperspectral data sets with high spatial resolution have been widely used in the research of image classification. The methodology based on the mathematical morphology, which aims at extracting the structure of hyperspectral images, has been implemented. In this method, opening and closing morphological operation used in hyperspectral data in order to retain spatial information of objects. Morphological profiles are established based on opening and closing transforms with structure element of different size. The proper definition of structure elements is the key of extracting morphological features for the image classification. Which kind of structural element is better for image classification. Can be discussed the classification results by setting different structural element to extract morphological features. In the experiments, we have defined four types of structure element to extract spatial features. Later on, the features are fed into a support vector machine (SVM) classifier respectively. The influence of different types of structure elements is judged by the classification accuracy. The experiment results illustrates disk shape of structural element is superior to diamond shape of structural element and the structural elements of large radius is better than the structural elements of small radius.