Large-scene precise classification of multispectral images (MSIs) has become one of the hot topics in the remote sensing field. MSIs usually have wide swath and a meter or even submeter level of spatial resolution, which make large-scene observation possible. However, the limited number of spectral bands leads to the confusion of land covers in classification, especially for the large-scene conditions with abundant land cover types. Therefore, overlapped hyperspectral images (HSIs) can be used to improve the precision degree of classification. To achieve this purpose, coupled dictionary learning has been proposed as a major means. Aiming at separating the class-specific characteristics and mutual patterns among different land covers, this article proposed a separable coupled dictionary learning (SCDL) method, which converts the separation of mutual features into the construction of separable coupled dictionaries and learns both class-specific coupled dictionaries and mutual coupled dictionaries simultaneously with the aid of label information. More specifically, the proposed method uses the labels of training samples to construct class-specific reconstruction error constraint, class-specificity constraint, and separable dictionary incoherence constraint as regularization terms, to make sure that the learned coupled dictionaries are to be both compact and discriminative. The learned separable coupled dictionaries facilitate pixels that belong to the same category to be represented by the mutual dictionary and the class-specific subdictionary of corresponding class. The experiments compared with several state-of-the-art methods on three pairs of HSI and MSI have shown better classification performance.