Pixel classification of remote sensing satellite image using semi-supervised clustering

Classifying the pixels of satellite images into homogeneous regions is a very challenging task as different regions have different types of land covers. Some land covers contain large regions, while some contain relatively smaller regions (eg. bridges, roads). In satellite image segmentation, no prior information is available about the number of clusters. Here, in this paper, we have solved this problem using the concepts of semi-supervised clustering which utilizes the property of unsupervised and supervised classification. Three cluster validity indices are utilized, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. First two cluster validity indices are symmetry distance based Sym-index and Euclidean distance based I-index, which are based on unsupervised properties. Last one is a supervised information based cluster validity index, Minkowski Index. For supervised information, initially Fuzzy C-mean clustering technique is used. Thereafter, based on the highest membership values of the data points with respect to different clusters, randomly 10% data points with their class labels are chosen. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on one Indian satellite image data set.