Paper Info
Title
A three-stage approach based on the self-organizing map for satellite image classification
Abstract
This work presents a methodology for the land-cover classification of satellite images based on clustering of the Kohonen's self-organizing map (SOM). The classification task is carried out using a three-stage approach. At the first stage, the SOM is used to quantize and to represent the original patterns of the image in a space of smaller dimension. At the second stage of the method, a filtering process is applied on the SOM prototypes, wherein prototypes associated to input patterns that incorporate more than one land cover class and prototypes that have null activity are excluded in the next stage or simply eliminated of the analysis. At the third and last stage, the SOM prototypes are segmented through a hierarchical clustering method which uses the neighborhood relation of the neurons and incorporates spatial information in its merging criterion. The experimental results show an application example of the proposed methodology on an IKONOS image.
Year
DOI
Venue
2007
10.1007/978-3-540-74695-9_70
ICANN (2)
Keywords
Field
DocType
satellite image,hierarchical clustering method,ikonos image,last stage,som prototype,classification task,proposed methodology,satellite image classification,self-organizing map,three-stage approach,land-cover classification,next stage,application example,self organizing maps,pattern recognition,spatial information,image processing,hierarchical clustering,remote sensing
Hierarchical clustering,Spatial analysis,Satellite,Pattern recognition,Computer science,Image processing,Filter (signal processing),Self-organizing map,Artificial intelligence,Cluster analysis,Land cover,Machine learning
Conference
Volume
ISSN
ISBN
4669
0302-9743
3-540-74693-5
Citations 
PageRank 
References 
0
0.34
10
Authors
3
Name
Order
Citations
PageRank
Márcio L. Gonçalves161.57
Márcio L. Netto2455.66
José Alfredo F. Costa35210.11