DocumentCode :
2127056
Title :
Approximate spectral clustering for unsupervised agriculture monitoring
Author :
Tasdemir, Kadim
Author_Institution :
Antalya International University, Department of Computer Engineering, Universite Caddesi No: 2, 07190, Dosemealti, Turkey
fYear :
2015
fDate :
20-24 July 2015
Firstpage :
396
Lastpage :
400
Abstract :
Unsupervised clustering methods produce land cover/use identification for monitoring agricultural resources with remote sensing, with no requirement of labeled training samples. Traditional methods, which are derived from some parametric models, are often insufficient for accurate identification. In contrast, approximate spectral clustering, a recently popular manifold learning algorithm depending on graph-cut optimization, extracts classes with various characteristics using a similarity criterion describing the data properties. We show in this paper that approximate spectral clustering, with advanced hybrid similarity criteria merging different information types, can achieve high accuracies for land cover classification to monitor agricultural resources in an unsupervised manner.
Keywords :
Accuracy; Agriculture; Monitoring; Neural networks; Quantization (signal); Remote sensing; Topology; agriculture monitoring; approximate spectral clustering; hybrid geodesic similarity; similarity criteria; unsupervised classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Agro-Geoinformatics (Agro-geoinformatics), 2015 Fourth International Conference on
Conference_Location :
Istanbul, Turkey
Type :
conf
DOI :
10.1109/Agro-Geoinformatics.2015.7248156
Filename :
7248156
Link To Document :
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