Recent advances in remote sensing have shown great potential for different kinds of applications such as vegetation and crop supervision. Using hyperspectral image processing, this process can be used to assess large areas of land, allowing for a fast and non-invasive analysis of variables such as water stress, diseases, and type of crops. In this context, representative spatial-spectral features are crucial for developing the data classification process of hyperspectral remote sensing images. In this way, extended attribute profiles (EAPs) are a powerful tool for efficiently capturing spatial and spectral properties. In addition, deep learning networks have shown a remarkable feature extraction performance in many applications, such as in the processing of hyperspectral images. In this paper, a data classification model is proposed that uses EAPs and an inception network to generate deep spatial-spectral features, which are labelled using a Riemannian classifier as a fine-tuning stage. This model was validated using three different datasets. Results obtained shows that the classification strategy proposed in this paper has better performance regarding the results generated by a traditional model of stacked convolutional neural networks (CNNs). Quantitatively speaking, the overall accuracy for the experiments with the proposed model and the tested data sets is greater than 99%.