DocumentCode
2938489
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
Volume
6
fYear
2003
fDate
21-25 July 2003
Firstpage
3745
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN
0-7803-7929-2
Type
conf
DOI
10.1109/IGARSS.2003.1295256
Filename
1295256
Link To Document