Recent advances in remote sensing have shown great potential for different kind of applications like vegetation and crop supervision. Using hyperspectral images (HSI), this process can be performed over large areas of land, allowing for a fast and non-invasive analysis of variables such as water stress, diseases, type of crops, among many others. In this context, representative spatial-spectral features are crucial to develop the data classification process of hyperspectral remote sensing images. In this way, the use of deep learning networks has shown a remarkable classification performance in many applications as the processing of hyperspectral images. In this paper, a data classification model that use kernel PCA (K-PCA) and inception network architecture to generate deep spatial-spectral features is proposed. The features generated are used to label the different kind of classes into the HSI. The labeling process uses a logistic regression (LR) algorithm. This model was validated using six different datasets. The experiments performed shown that the proposed strategy allows improving the data classification performance regarding the results obtained by the traditional stacked layer model of convolutional neural networks (CNN). Quantitatively speaking, the overall accuracy over all experiments of the proposed model for all tested datasets is greater than 92%.