HIERARCHICAL CLUSTERING ALGORITHMS FOR SEGMENTATION OF MULTISPECTRAL IMAGES
Computationally efficient HCA and HECA hierarchical clustering algorithms for segmentation of multispectral images have been developed using the grid and ensemble approaches. A special metric is proposed to identify embedded clusters even in the presence of overlapping. The efficiency of the algorithms has been confirmed by the results of experimental studies using model and real data.