JIP_V2_N2_RP3 Keerthika S. Sivaranjini R. Journal on Image Processing 2349-6827 2 2 14 19 Harmonic Analysis (HA); Hyper Spectral Image Classification (HSI); Particle Swarm Optimization (PSO); Relevance Vector machine (RVM) Image processing plays a vital role in all fields like satellite, medical, telecommunication, and missile. Hyper spectral images show similar statistical properties to natural grayscale or color photographic images. HSI (Hyper Spectral Image) are a more challenging area because of high spectral bands and dimensionality. It is also very easy to learn. It is used to identify the problems in various fields like Signal Processing, and moreover used to determine the complex manifolds. There are several algorithms which have been proposed to classify the hyper spectral image. In our paper new methods have been introduced, that is Harmonic Analysis based classification such as HA-PSO-RVM (Particle swarm Optimization – Relevance Vector Machine) approach. This new approach accurately classifies the cluster band with respect to their amplitude and phase. Harmonic Analysis (HA) is introduced to extract the features of hyper spectral image. Amplitude and phase features have been obtained by deriving HA. Then the best features are selected, among extracted features by Particle swarm Optimization. Finally, the respective bands are classified by a related cluster, which are performed by the help of Relevance Vector Machine (RVM). This classifier accurately classifies the band to respective cluster form. In prior work, instead of HA, the used MNF, PCA and ICA could extract features and also in combination of PSO-SVM could use CV-SVM and GA-SVM. Here, the process will be carried out by integrating HA-PSO-RVM. This combination leads to provide good accuracy and also limited computer time because of the usage of the PSO Method. April - June 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3402