Hyperspectral image (HSI) classification is very important task having numerous applications in the remote sensing field. Many methods have been proposed in the recent years. Among them Convolutional Neural Network (CNN) based algorithms have shown higher performance. But these algorithms need high computational power and storage capacity. This Paper presents an approach for remote sensing hyper spectral image classification based on data normalization and CNN. HSI data is first normalized by reducing its scalar values by retaining complete information. Then, spectral and spatial information is extracted using Probabilistic Principal Component Analysis (PPCA) and Gabor filtering respectively. Further, the spectral and spatial information is integrated to form fused features. Finally classification task is done using simply designed CNN framework. Experiments are performed on three benchmark hyperspectral datasets (Indian Pines, Pavia University and Salinas). The proposed approach has achieved significant performance over the state-of-art methods. This can be useful in real world applications like agriculture, forestry and food processing. © 2020 Elsevier B.V.