Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery

This paper presents an approach for improving performances of the unsupervised classification (clustering) in remote sensing imagery by proposing a technique to combine the classical techniques of K-means clustering and the Gaussian mixture model (GMM) with expectation-maximization (EM). The proposed model means to apply firstly K-means clustering, and to use the result of this technique to compute means μ_k, covariance matrices Σ_k and mixing coefficients π_k, considered as initialization parameters for the next stage of GMM-EM. The performances of the proposed algorithm for remote sensing image classification using a LANDSAT 7 ETM+ dataset are evaluated. The proposed combined clustering model K-means+GMM-EM, has led to a significant improvement in performance over any of the two single clustering techniques K-means and GMM-EMM.