Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Sparse Inverse Covariance Estimates for Hyperspectral Image Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Berge, A. ; Dept. of Informatics, Oslo Univ. ; Jensen, A.C. ; Solberg, A.H.S.

Classification of remotely sensed hyperspectral images calls for a classifier that gracefully handles high-dimensional data, where the amount of samples available for training might be very low relative to the dimension. Even when using simple parametric classifiers such as the Gaussian maximum-likelihood rule, the large number of bands leads to copious amounts of parameters to estimate. Most of these parameters are measures of correlations between features. The covariance structure of a multivariate normal population can be simplified by setting elements of the inverse covariance matrix to zero. Well-known results from time series analysis relates the estimation of the inverse covariance matrix to a sequence of regressions by using the Cholesky decomposition. We observe that discriminant analysis can be performed without inverting the covariance matrix. We propose defining a sparsity pattern on the lower triangular matrix resulting from the Cholesky decomposition, and develop a simple search algorithm for choosing this sparsity. The resulting classifier is used on four different hyperspectral images, and compared with conventional approaches such as support vector machines, with encouraging results

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:45 ,  Issue: 5 )