By Topic

Remote sensing data analysis by Kohonen feature map and competitive learning

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

4 Author(s)
Nogami, Y. ; Dept. of Comput. & Syst. Sci., Osaka Prefectural Univ., Sakai, Japan ; Jyo, Y. ; Yoshioka, M. ; Omatu, S.

In this paper as a preprocessing of land use classifications, we use the Kohonen feature map (KFM) and the competitive learning (CL) to get the better training data set. At a first step, the KFM that takes the Landsat TM data as input is adopted to form a rough classification of the wide area based on the observed data. At the next step, the CL whose inputs are the weights of the KFM node data is carried out to determine the category of each node of the KFM. The first weight set of the CL is taken as the weights at the corner nodes of the KFM. The combination of the two neural network techniques enables us to determine the rough land-use of an object region automatically. After that, the classification results by the KFM and the CL are further classified into more fine items by using the backpropagation method. Finally, the classification results have been compared with other methods

Published in:

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on  (Volume:1 )

Date of Conference:

12-15 Oct 1997