Hyperspectral image xlassification using a general NFLE transformation with kernelization and fuzzification
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Date issued
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Václav Skala - UNION Agency
Abstract
Nearest feature line (NFL) embedding (NFLE) is an eigenspace transformation algorithm based on the NFL
strategy. Based on this strategy, the NFLE algorithm generates a low dimensional space in which the local
structures of samples in the original high dimensional space are preserved. Though NFLE has successfully
demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently
by linear scatters using the linear NFLE method. To address this, a general NFLE transformation, called
fuzzy/kernel NFLE, is proposed for feature extraction in which kernelization and fuzzification are simultaneously
considered. In the proposed scheme, samples are projected into a kernel space and assigned larger weights based
on that of their neighbors according to their neighbors. In that way, not only is the non-linear manifold structure
preserved, but also are the discriminative powers of classifiers increased. The proposed method is compared with
various state-of-the-art methods to evaluate the performance by several benchmark data sets. From the
experimental results, the proposed FKNFLE outperformed the other, more conventional, methods.
Description
Subject(s)
hyperspektrální klasifikace obrazů, rozmanité učení, kernelizace, fuzzifikace
Citation
WSCG '2015: short communications proceedings: The 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2015 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic8-12 June 2015, p. 75-82.