Title :
An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model
Author :
Acito, N. ; Corsini, G. ; Diani, M.
Author_Institution :
Dipt. di Ingegneria dell´´Inf., Pisa Univ., Italy
Abstract :
A new algorithm for hyperspectral image segmentation based on the statistical approach is presented. The algorithm is completely unsupervised and relies only on the spectral information. The hyperspectral image is statistically characterized by means of the Gaussian Mixture Model (GMM). Preliminary results obtained on experimental data are presented and discussed.
Keywords :
geophysical signal processing; image segmentation; statistical analysis; Gaussian mixture model; algorithm; hyperspectral image segmentation; spectral information; statistical analysis; Algorithm design and analysis; Clustering algorithms; Covariance matrix; Gaussian distribution; Hyperspectral imaging; Hyperspectral sensors; Image segmentation; Layout; Pixel; Probability density function;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1295256
