Generally, the objects in an image have complex shapes and sizes. Therefore, it is difficult for the existing morphological features to completely describe the significant spatial information of the image. Hence, a morphological saliency profile is developed in this study based on the saliency measure. The grayscale and contour information of a particular area can be used to estimate the value of the saliency measure. This measure is used to describe the importance of a target in a scene. Thus, the important area of an image can be extracted based on the local maximum value of the saliency measure, and its spatial information can be obtained based on the multi-level features. When extracting the morphological saliency profile, attribute filtering based on the saliency measure is performed to eliminate redundant image details and retain the saliency profile of the image. Subsequently, the hierarchical spatial features are generated according to the saliency of the organization structure in the image. Two hyperspectral datasets are used in this experiment for verification. The experimental results demonstrate that the classification performance of the proposed algorithm is superior to those of the existing morphological feature extraction algorithms.