Paper Info
Title
Cluster Analysis of Land-Cover Images Using Automatically Segmented SOMs with Textural Information
Abstract
This work attempts to take advantage of the properties of Kohonen's Self-Organizing 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.
Year
DOI
Venue
2008
10.1007/978-3-540-88906-9_61
IDEAL
Keywords
Field
DocType
image data,tm-landsat image,different segmentation,clustering method,neuron-distance image,image categorization process,data clustering,different partition,automatically segmented soms,cluster analysis,textural information,trained som,mathematical morphology,indexation,image processing,remote sensing
Cluster (physics),Computer science,Image processing,Self-organizing map,Artificial intelligence,Cluster analysis,Land cover,Categorization,Computer vision,Pattern recognition,Mathematical morphology,Pixel,Machine learning
Conference
Volume
ISSN
Citations 
5326
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Márcio L. Gonçalves161.57
Márcio L. Netto2455.66
José A. Costa300.34