DocumentCode :
3644071
Title :
Neural network based approximate spectral clustering for remote sensing images
Author :
Kadim Taşdemir
Author_Institution :
European Commission Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027, Ispra, Italy
fYear :
2011
fDate :
7/1/2011 12:00:00 AM
Firstpage :
2884
Lastpage :
2887
Abstract :
Contrary to the traditional clustering methods (often based on parametric models), a recently popular non-parametric method, spectral clustering (SC), employs eigendecomposition of pairwise similarities, and has been shown successful. Despite the advantages of spectral clustering, due to its computational and spatial complexity, its use in remote sensing applications is possible only through approximate spectral clustering (ASC), i.e. SC of the data representatives obtained by quantization or sampling. In this study, we show that, compared to other quantization methods, neural network (self-organizing map or neural gas) based quantization produces better quantization for ASC, to achieve high clustering accuracies.
Keywords :
"Accuracy","Quantization","Remote sensing","Laplace equations","Clustering algorithms","Self organizing feature maps"
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Electronic_ISBN :
2153-7003
Type :
conf
DOI :
10.1109/IGARSS.2011.6049817
Filename :
6049817
Link To Document :
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