Hyperspectral imaging (HSI) collects hundreds of images over large spatial observation areas on the Earth's surface, recording scenes at different wavelength channels and providing a vast amount of information. Recurrent neural networks (RNNs) have been widely used for the classification of HSI datasets, understood as a single sequence of pixel vectors with high dimensionality. However, the RNN model scales poorly when dealing with HSI scenes with large dimensionality. In order to mitigate this problem, this paper presents a new RNN classifier based on simple recurrent units (SRUs) that performs HSI classification in a highly scalable and efficient way. Our experimental results (conducted on four real HSI datasets) reveal very good performance, not only in terms of classification accuracy (in line with existing methods), but also in terms of computational performance when dealing with large datasets.