Classification of real-world remote sensing images is a challenging task because of complex spectral–spatial information with high-dimensional feature vectors. Most of the traditional classification approaches directly treat data as vectors, which usually results in a small sample size problem and abundant redundant information; thus, they inevitably degrade the performance of the classifier. To overcome the drawbacks, we take advantage of the benefits of local scatters and tensor representation and propose a framework for hyperspectral image (HSI) classification through combining local tensor discriminant analysis (LTDA) with spectral–spatial feature extraction. First, we use a well-known spectral–spatial feature extraction approach to extract abundant spectral–spatial features as feature tensors. Then, based on class label information, LTDA is used to eliminate redundant information and to extract discriminant feature tensors for the subsequent classification. Two real HSIs are used as experimental datasets. The obtained results indicate that the proposed method exhibits good performance, while using a small number of training samples.