Application of competitive neural networks for unsupervised analysis of hyperspectral remote sensing images

Monica Tellechea; Manuel Grana Romay

doi:10.1117/12.413882

19 January 2001 Application of competitive neural networks for unsupervised analysis of hyperspectral remote sensing images

Monica Tellechea, Manuel Grana Romay

Author Affiliations +

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413882
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

We study the application of Competitive Neural Networks (CNN) to the Unsupervised analysis of Remote Sensing Hyperspectral images. CNN are applied as clustering algorithms at the pixel level. We propose their use for the extraction of endmembers and evaluate them through the error induced by the compression/decompression with the CNN in the supervised classification of the images. We show results with the Self Organizing Map and Neural Gas applied to a well known case study.

Citation Download Citation

Monica Tellechea and Manuel Grana Romay "Application of competitive neural networks for unsupervised analysis of hyperspectral remote sensing images", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413882

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