Traditional feature extraction algorithms consider only spectral information in the hyperspectral image (HSI) and cannot extract fine spatial information. To solve this problem, this paper proposes a supervised spatially-regularized manifold discriminant analysis (SSRMDA) algorithm to improve the classification performance of ground objects in the HSI. The SSRMDA algorithm firstly constructs a spectral-domain intraclass image and an interclass image by using the label information of training samples, which reveals the potential nonlinear manifold structure of hyperspectral data. Based on that, a spatial-domain intraclass image is constructed, and it combines the spectral information of HSI by regularization to realize the effective fusion of spectral-spatial information. In low-dimensional space, the intraclass data in low dimensional space becomes more clustered and the separability of embedded features is enhanced. Experiments on the Indian Pines and Washington DC Mall datasets show that the overall classification accuracy of the SSRMDA algorithm reaches 91.58% and 96.67%, respectively, which denotes that the proposed algorithm effectively improves the classification ability of ground objects. Compared with other feature extraction algorithms, the proposed algorithm is effective in practical applications, especially when a small number of training samples are available.