In this paper a self-paced learning-based probability subspace projection (SL-PSP) method is proposed for hyperspectral image classification. First, a probability label is assigned for each pixel, and a risk is assigned for each labeled pixel. Then, two regularizers are developed from a self-paced maximum margin and a probability label graph, respectively. The first regularizer can increase the discriminant ability of features by gradually involving the most confident pixels into the projection to simultaneously push away heterogeneous neighbors and pull inhomogeneous neighbors. The second regularizer adopts a relaxed clustering assumption to make avail of unlabeled samples, thus accurately revealing the affinity between mixed pixels and achieving accurate classification with very few labeled samples. Several hyperspectral data sets are used to verify the effectiveness of SL-PSP, and the experimental results show that it can achieve the state-of-the-art results in terms of accuracy and stability.