NDLI: Nonlinear dimension reduction methods and segmentation of hyperspectral images

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Nonlinear dimension reduction methods and segmentation of hyperspectral images

Content Provider	IEEE Xplore Digital Library
Author	Bilgin, G. Erturk, S. Yildirim, T.
Copyright Year	2008
Abstract	In this paper nonlinear dimension reduction methods are applied to hyperspectral images and the segmentation performance is investigated. The biggest disadvantage of nonlinear dimension reduction techniques is their long computational processing time. To overcome this problem, prototypes which represent the spectral distribution of the scene have been obtained with vector quantization and dimension reduction has been applied on these prototypes. Dimension reduction of all pixels in the scene has been accomplished using Radial Basis Function (RBF) neural networks and the developed dasiaK-point mean interpolationpsila method. The positive effects of these methods on the segmentation of the scene have been presented in the experimental results section using objective evaluation criterion.
Starting Page	1
Ending Page	4
File Size	711652
Page Count	4
File Format	PDF
ISBN	9781424419982
DOI	10.1109/SIU.2008.4632577
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-20
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Hyperspectral sensors Prototypes Artificial neural networks Remote sensing Hyperspectral imaging Principal component analysis
Content Type	Text
Resource Type	Article

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