Collaborative representation classifier (CRC) is an efficient classifier for hyperspectral imagery. It represents a testing sample using labeled ones, and the testing sample is assigned to the class whose labeled samples yield the minimum representation error. The CRC allows all the samples to have equal chance to participate in the representation by imposing an L2 norm minimization constraint. The solution has a closed form, offering computational convenience. Various techniques have been developed for further improvement of CRC-based classifiers, and probabilistic collaborative representation-based classifier (ProCRC) is one of techniques to enhance CRC by using maximum likelihood concept of testing sample that belongs to multiple classes. Taking into consideration for distance-weighted Tikhonov regularization, probabilistic collaborative representation-based classifier with Tikhonov regularization (ProCRT) can enhance the performance of the original ProCRC. In this paper, spatial regularization term is added in the objective function to incorporate spatial information, and the resulting spatial-aware ProCRC (SaProCRC) and spatial-aware ProCRT (SaProCRT) can offer even better classification accuracy with comparable computational cost.