Clustering in Remote Sensing Using an Unsupervised Neural Network

An unsupervised neural network based on a new neural unit is applied to the problem of clustering a data set of pixels drawn through remote sensing of a limited portion of the terrestrial surface. The aim of the partition is to split the sensed area into sets having roughly the same type of ground cover. After the partitioning a comparison with the ground truth has shown that the unsupervised net has been able to split accurately the given data set in subsets similar to the classes really observable with a fast convergence and a high resolution.