Cluster Analysis of Land-Cover Images Using Automatically Segmented SOMs with Textural Information

This work attempts to take advantage of the properties of Kohonen's Self-Organizingg Map (SOM) to perform the cluster analysis of remotely sensed images. A clustering method which automatically finds the number of clusters as well as the partitioning of the image data is proposed. The data clustering is made using the SOM. Different partitions of the trained SOM are obtained from different segmentations of the U-matrix (a neuron-distance image) that are generated by means of mathematical morphology techniques. The different partitions of the trained SOM produce different partitions for the image data which are evaluated by cluster validity indexes. Seeking to guarantee even greater efficiency in the image categorization process, the proposed method extracts information from the image by means of pixel windows, in order to incorporate textural information. The experimental results show an application example of the proposed method on a TM-Landsat image.