  There are many challenges in the classification of hyper spectral images such
as large dimensionality, scarcity of labeled data and spatial variability of
spectral signatures. In this proposed method, we make a hybrid classifier
(MLP-SVM) using multilayer perceptron (MLP) and support vector machine (SVM)
which aimed to improve the various classification parameters such as accuracy,
precision, recall, f-score and to predict the region without ground truth. In
proposed method, outputs from the last hidden layer of the neural net-ork
become the input to the SVM, which finally classifies into various desired
classes. In the present study, we worked on Indian Pines, U. Pavia and Salinas
dataset with 16, 9, 16 classes and 200, 103 and 204 reflectance bands
respectively, which is provided by AVIRIS and ROSIS sensor of NASA Jet
propulsion laboratory. The proposed method significantly increases the accuracy
on testing dataset to 93.22%, 96.87%, 93.81% as compare to 86.97%, 88.58%,
88.85% and 91.61%, 96.20%, 90.68% based on individual classifiers SVM and MLP
on Indian Pines, U. Pavia and Salinas datasets respectively.
