A new unsupervised approach for the classification of multispectral data
In this paper, we present a new approach for unsupervised pixel-wise classifcation of multispectral data. In this approach, we estimate the number of classes in a data set as well as the parameters of each class. This approach doesn't require training samples. A scene is modeled as a realization of a random field formed of a mixture distribution. We employ an EM algorithm to estimate the parameters of this mixture. The algorithm iterates to estimate the proper number of classes that compose the scene, and also the parameters of these classes. Theoretically, the algorithm can be applied for arbitrary forms of distributions for the classes in the scene, either parametric or non parametric. This paper focuses on the parametric estimation case. The approach has been applied to synthetic and real world multispectral data and provided quite encouraging results compared to other classical classification approaches.