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

摘要

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 μk, covariance matrices Σk and mixing coefficients πk, 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.