Hyperspectral imaging (HSI) classification has recently become a field of interest in the remote sensing (RS) community. However, such data contain multidimensional dynam ic features that make it difficult for precise identification. Also, it covers structurally nonlinear affinity within the gathered spectral bands and the related materials. To systematically facilitate the HSI categorization, we propose a spectral-spatial classification of HSI data using a 3D -2D convolutional neural network and inception network to extract and learn the in -depth spectral-spatial feature vectors. We first applied the principal component analysis (PCA) on the entire HSI image to reduce the or iginal space dimensionality. Second, the exploitation of the spatial hyperspectral i nput features contiguous information by 2 -D CNN. Besides, we used 3 -D CNN without relying on any preprocessing to extract deep spectral -spatial fused features efficiently. The learned spectral-spatial characteristics are concatenated and fed to the incepti on network layer for joint spectral -spatial learning. Furthermore, we learned and achieved the correct classification with a softmax regression classifier. Finally, we eval uated our model performance on different training set sizes of two hyperspectral rem ote sensing data sets (HSRSI), namely Botswana (BT) and Kennedy Space Center (KSC), and compared the experimental results with deep learning -based and state-of-the-art (SOT A) classification methods. The experiment results show that our model provides competitive classification results with state -of-the-art techniques, demonstrating the considerable potential for HSRSI classification.