Band clustering using expectation-maximization algorithm and weighted average fusion-based feature extraction for hyperspectral image classification

The presence of a significant amount of information in the hyperspectral image makes it suitable for numerous applications. However, extraction of the suitable and informative features from the high-dimensional data is a tedious task. A feature extraction technique using expectation–maximization (EM) clustering and weighted average fusion technique is proposed. Bhattacharya distance measure is used for computing the distance among all the spectral bands. With this distance information, the spectral bands are grouped into the clusters by employing the EM clustering method. The EM algorithm automatically converges to an optimum number of clusters, thereby specifying the absence of need for the required number of clusters. The bands in each cluster are fused together applying the weighted average fusion method. The weight of each band is calculated on the basis of the criteria of minimizing the distance inside the cluster and maximizing the distance among the different clusters. The fused bands from each cluster are then considered as the extracted features. These features are used to train the support vector machine for classification of the hyperspectral image. The performance of the proposed technique has been validated against three small-size standard bench-mark datasets, Indian Pines, Pavia University, Salinas, and one large-size dataset, Botswana. The proposed method achieves an overall accuracy (OA) of 92.19%, 94.10%, 93.96%, and 84.92% for Indian Pines, Pavia University, Salinas, and Botswana datasets, respectively. The experimental results prove that the proposed technique attains significant classification performance in terms of the OA, average accuracy, and Cohen’s kappa coefficient (k) when compared to the other competing methods.