A New Semi-Supervised Classification Method Based on Mixture Model Clustering for Classification of Multispectral Data

Abstract

A new method for semi-supervised classification of remotely-sensed multispectral image data is developed in this study. It consists of unsupervised-clustering for data labelling and supervised-classification of clusters in multispectral image data (MID) using spectral signatures. Mixture model clustering, based on model selection, is proposed for finding the number and determining the structures of clusters in MID. The best mixture model, for the best clustering of data, finds the number and determines the structure of clusters in MID. The number of elements in the best mixture model fits to the number of clusters in MID. The elements of the best mixture model fits to the structure of clusters in MID. Clusters in MID is supervised-classified using spectral signatures. Euclidean distance is used as the discrimination function for the supervised-classification method. The values of Euclidean distances are used as decision rule for the supervised-classification method.