DocumentCode
2677994
Title
Hyperspectral image classification with mahalanobis relevance vector machines
Author
Camps-Valls, Gustavo ; Rodrigo-González, Antonio ; Muñoz-Marí, Jordi ; Gómez-Chova, Luis ; Calpe-Maravilla, Javier
Author_Institution
Univ. de Valencia, Valencia
fYear
2007
fDate
23-28 July 2007
Firstpage
3802
Lastpage
3805
Abstract
This paper introduces the use of Relevance Vector Machines (RVM) for remote sensing hyperspectral image classification. We also include the Mahalanobis kernel in the formulation of the RVM to take into account the covariance of the features in the classification process. Experimental results in different scenarios confirm the accuracy and robustness of the proposed method, and also the ease of free parameters tuning.
Keywords
geophysical techniques; image classification; Mahalanobis kernel; RVM; Relevance Vector Machines; features covariance; hyperspectral image classification; Bayesian methods; Gaussian processes; Hyperspectral imaging; Hyperspectral sensors; Image classification; Kernel; Remote sensing; Robustness; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location
Barcelona
Print_ISBN
978-1-4244-1211-2
Electronic_ISBN
978-1-4244-1212-9
Type
conf
DOI
10.1109/IGARSS.2007.4423671
Filename
4423671
Link To Document