Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. There are several algorithm has been proposed to classify the hyperspectral image. In our project a new novel method has been introduced that is Principle Component Analysis based classification such as PCA-PSO-SVM approach. This new approach accurately classify the cluster band with respect to their amplitude and phase. Principle Component Analysis (PCA) is introduced to extract the feature from hyperspectral image. Amplitude and phase features has been obtained by derived PCA. Then select best feature among extracted feature by Particle Swarm Optimization. Finally, classify the respective band with related cluster which is performed with the help of Support Vector Machine (SVM). This classifier accurately classify the band to respective cluster form. In future work, this process will be carried out by integrating PCA-BFO-RVM. This combination leads to provide good accuracy and also limited computational time because of using the BFO (Bacterial foraging optimization Algorithm) can obtain less computation time compared with our proposed approach.