By Topic

Hypertemporal Classification of Large Areas Using Decision Fusion

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
$33 $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

5 Author(s)
Udelhoven, T. ; Centre de Rech. Public Gabriel Lippmann, Belvaux ; van der Linden, S. ; Waske, B. ; Stellmes, M.
more authors

A novel multiannual land-cover-classification scheme for classifying hypertemporal image data is suggested, which is based on a supervised decision fusion (DF) approach. This DF approach comprises two steps: First, separate support vector machines (SVMs) are trained for normalized difference vegetation index (NDVI) time-series and mean annual temperature values of three consecutive years. In the second step, the information of the preliminary continuous SVM outputs, which represent posterior probabilities of the class assignments, is fused using a second-level SVM classifier. We tested the approach using the 10-day maximum-value NDVI composites from the ldquoMediterranean Extended Daily One-km Advanced Very High Resolution Radiometer Data Setrdquo (MEDOKADS). The approach increases the classification accuracy and robustness compared with another DF method (simple majority voting) and with a single SVM expert that is trained for the same multiannual periods. The results clearly demonstrate that DF is a reliable technique for large-area mapping using hypertemporal data sets.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 3 )