Despite the availability of an increasing amount of remote sensing images, problems still arise in that the knowledge from existing images is underutilized and the collection of reference knowledge for each newly obtained image is expensive. Recently, an attractive solution called “transfer learning” has received increasing attention in the remote sensing field, by transferring knowledge from source domains to help improve the learning procedure in the target domain. In this paper, we propose a sparse subspace correlation analysis-based supervised classification (SSCA-SC) method for transfer learning in hyperspectral remote sensing image classification, which is not restricted by the data dimensionality or the data acquisition sensors. Specifically, we first propose a sparse subspace correlation analysis (SSCA) method to simultaneously learn the optimal projection matrices for heterogeneous domains into a common subspace and obtain sparse reconstruction coefficients over a shared self-expressive dictionary in the derived subspace. In order to fully utilize the label information to improve the class separability, the SSCA-SC framework learns more discriminative representations for the input data by training a corresponding SSCA model for each class. As a result, the projected data belonging to the same class are maximally correlated and represented well, while those from different classes will have a low correlation. Another advantage of the SSCA-SC framework lies in the fact that it not only learns new representations for the data from different domains but it also designs a discriminative and robust classifier that properly adapts to the new representation. The proposed method was tested with three hyperspectral remote sensing data sets, and the experimental results confirm the effectiveness and reliability of the proposed SSCA-SC method.