A Spatial Gaussian Mixture Model for Optical Remote Sensing Image Clustering

Clustering has always been one of the most challenging tasks in optical remote-sensing image (ORSI) processing, as a result of the intrinsic complexity of the distribution of the ground objects. The Gaussian mixture model (GMM), as a traditional, effective clustering method, has been widely applied. However, the traditional model does not take the spatial information into consideration. To solve the problem, a new model named the spatial Gaussian mixture model (SGMM) is proposed for ORSI clustering. The SGMM can incorporate the spatial information by generating spatial windows around pixels. An estimation algorithm based on expectation-maximization (EM) is also developed to estimate the parameters of the SGMM. The relationships between the SGMM/GMM and the SGMM/probabilistic latent semantic analysis (PLSA) are analyzed theoretically. The proposed SGMM can be considered to be an extension of the GMM and a continuous version of PLSA. In addition, two methods based on the SGMM are proposed to infer the cluster labels of the pixels. One method is based on the maximum likelihood rule, and is called SGMM-MLR, while the other method combines the SGMM and conditional random fields (CRF), and is called SGMM-CRF. The experimental results with three remote-sensing images show that the proposed clustering method based on the SGMM can improve the performance of clustering for ORSIs, compared to k-means, fuzzy c-means (FCM), and the GMM. It is also able to acquire a better performance than the latest cluster methods with spatial information, such as kernel weighted fuzzy local information c-means (KWFLICM), and the GMM coupled with CRF.