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Comparison of CBF, ANN and SVM classifiers for object based classification of high resolution satellite images

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Title Comparison of CBF, ANN and SVM classifiers for object based classification of high resolution satellite images
 
Creator BUDDHIRAJU, KM
RIZVI, IA
 
Subject neural-network
recognition
object based image classification
ann
svm
radial basis functions
high resolution satellite images
 
Description Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison, adaptive Gaussian filtered images were classified using ANN and post-processed using relaxation labeling process (RLP). The results are demonstrated using high spatial resolution remotely sensed images.
 
Publisher IEEE
 
Date 2011-10-24T20:33:40Z
2011-12-15T09:11:44Z
2011-10-24T20:33:40Z
2011-12-15T09:11:44Z
2010
 
Type Proceedings Paper
 
Identifier 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM,40-43
978-1-4244-9566-5
http://dx.doi.org/10.1109/IGARSS.2010.5652033
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15517
http://hdl.handle.net/100/2258
 
Source IEEE International Geoscience and Remote Sensing Symposium,Honolulu, HI,JUN 25-30, 2010
 
Language English