Neural Network based Adaboosting Approach for Hyperspectral Data Classification

In this paper, we propose a novel approach for hyperspectral data classification using adaboosting of artificial neural networks based weak classifiers. The adaboost algorithm employs an iterative approach which combines weak classifiers to approximate a Bayes classifier. It requires performance of each weak classifier to be a little better than random guessing. In the approach, we use several neural network based weak classifiers to make one strong classifier and each weak classifier contains only one hidden layer. As the weak classifiers are simple, therefore, these does not require a lot of time for training and, therefore, are time efficient. Back propagation algorithm is used for the learning purposes. In the approach, weights are assigned to each training sample and those are boosted in subsequent iterations for the misclassified training samples, therefore, providing more robust approach to classify difficult samples. A confidence score is assigned to each weak classifier. Final classifier is achieved by linear combination of all the weak classifiers. Entropy gain clustered PCA is used for the dimension reduction of the data. We performed experiments on real hyperspectral dataset of AVIRIS Indian Pines and the comparisons with neural network and ML based approaches prove the efficiency of the proposed approach.