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Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most CR classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and heterogeneity problems. Therefore, this article proposes a mean-weighted CR classification model (MWCRC) based on the joint spatial-spectral data. It imposes mean and weighted constraints on the representation coefficients based on CR, which attenuates the noise effect and increases the distinguishability between classes. Second, a sample augmentation method based on the principle of minimizing the representation residuals is proposed. Sample augmentation is realized through initial classification and calculation of representation residuals to achieve the objective of consolidating model stability and improving classification accuracy. Meanwhile, in order to alleviate the problem of spatial homogeneity and heterogeneity, the extended morphological profile (EMP) and the stacking approach are utilized to construct the joint spatial-spectral data for the classification of MWCRC. The superiority of the proposed method is demonstrated by experimental validation using a small number of training samples in three real datasets.