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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.
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on (Volume:4 )
Date of Conference: 19-21 May 2009