Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology-based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology-based structure-preserving projection (MSPP) and tries to preserve the data structure in spectral–spatial feature space. Moreover, MSPP increases the class discrimination ability by defining a similarity matrix constructed by extended spectral–spatial training samples. The experimental results show the superiority of MSPP compared to some state-of-the-art classification methods from the classification accuracy point of view.