Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis
Self-Organizing neural networks
Self-Organizing Maps
Mining Patterns of Change in Remote Sensing Image Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Expectation-Maximization x Self-Organizing Maps for Image Classification
SITIS '08 Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
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Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions differentiate land uses by classification algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps (SOM) algorithm, which exploits the spatial correlation among near pixels. The neurons in the SOM will cluster the objects found in the image, and such objects will define the image segments.