Automatic remotely sensed data clustering by tree-structured self-organizing maps

This work presents a clusters analysis method which automatically finds the number of clusters as well as the partitioning of data set in a remotely sensed image without any type of assistance of an image analyst. The data clustering is made using the self-organizing (or Kohonen) map (SOM) and the techniques proposed by Costa & Netto (2001) for automatic partition of trained SOM networks and for generating a hierarchy of maps based on the detected data clusters. The proposed clustering method has been applied on a LANDSAT/TM image and its performance was compared with that of K-means algorithm, conventionally used for remotely sensed images.