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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/2258

Title: Comparison of CBF, ANN and SVM classifiers for object based classification of high resolution satellite images
Authors: BUDDHIRAJU, KM
RIZVI, IA
Keywords: neural-network
recognition
Issue Date: 2010
Publisher: IEEE
Citation: 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM,40-43
Abstract: 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.
URI: 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
ISBN: 978-1-4244-9566-5
Appears in Collections:Proceedings papers

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