By Topic

Wetland Extraction in Sanjiang Plain Based on Self-Organized Feature Map Neural Network Clustering Model

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

3 Author(s)
Liu Hanli ; Key Lab. of Geographic Inf. Sci., East China Normal Univ., Shanghai, China ; Pei Tao ; Zhou Chenghu

By analyzing spectral characteristics of MODIS remote sensing data in Sanjiang Plain, we extract the wetland in this area based on a set of multi-temporal and multi-spectral MODIS data. We first transform the selected data by a minimum noise fraction (MNF) rotation and take the first two components of the transformed data as the experimental data. To improve the accuracy of classification of wetland, we use a self-organization feature map (SOM) neural network model. SOM has a good performance in resisting noise and can be implemented with parallel processing technique. It is capable of keeping the topological structure of the original data. As a result, it may achieve a better classification compared with other clustering models. After clustering, we perform a discrete wavelet transform (DWT) to smooth the data and eliminate noise from the data. The result shows that the SOM model is effective and the clustering result has been improved.

Published in:

2009 WRI Global Congress on Intelligent Systems  (Volume:4 )

Date of Conference:

19-21 May 2009