Hyperspectral Image Classification Using Harmonic Analysis Integrated with BFO Optimized SVM

The classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labelled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. In our project a new novel method has been introduced that is Harmonic Analysis based classification such as HA-BFO-SVM approach. This new approach accurately classifies the cluster band with respect to their amplitude and phase. Harmonic Analysis (HA) is introduced to extract the feature from hyperspectral image. Amplitude and phase a feature has been obtained by derived HA. Then select best feature among extracted feature by Bacterial Foraging Optimization (BFO). Finally, classify the respective band with related cluster which is performed with the help of Support Vector Machine (SVM). This classifier accurately classifies the band to respective cluster form. In prior work, instead of HA, used MNF, PCA, and ICA could extract features and also classification has been performed by BFO-SVM instead of using PSO-SVM, CVSVM and GA-SVM.