By Topic

Remotely sensed data analysis using two neural networks and its application to land cover mapping

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)
Murai, H. ; Shikoku Univ., Tokushima, Japan ; Omatu, S. ; Oe, S.

In recent works, the authors have proposed a hybrid system using a Kohonen's self-organization feature mapping preprocessor (SOM) and a multi-layered neural network processor (BPM) to analyze remotely sensed data, and demonstrated the applicability of SOM preprocessor by a principal component analysis (PCA). In the present paper, the authors empirically examine the significance of the principal components for the input pattern

Published in:

Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International  (Volume:1 )

Date of Conference:

6-10 Jul 1998