Application of competitive neural networks for unsupervised analysis of hyperspectral remote sensing images
Monica Tellechea, Manuel Graña · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
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.