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Spherical stochastic neighbor embedding of hyperspectral data

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dc.contributor.author Lunga, D
dc.contributor.author Ersoy, O
dc.date.accessioned 2013-04-11T10:27:36Z
dc.date.available 2013-04-11T10:27:36Z
dc.date.issued 2012-07
dc.identifier.citation Lunga, D and Ersoy, O. 2012. Spherical stochastic neighbor embedding of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, vol. 51(2), pp 857- 871 en_US
dc.identifier.issn 0196-2892
dc.identifier.uri http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6249739
dc.identifier.uri http://hdl.handle.net/10204/6655
dc.description Copyright: 2012 IEEE Xplore. This is an ABSTRACT ONLY. The definitive version is published in IEEE Transactions on Geoscience and Remote Sensing, vol. 51(2), pp 857- 871 en_US
dc.description.abstract In hyperspectral imagery, low-dimensional representations are sought in order to explain well the nonlinear characteristics that are hidden in high-dimensional spectral channels. While many algorithms have been proposed for dimension reduction and manifold learning in Euclidean spaces, very few attempts have focused on non-Euclidean spaces. Here, we propose a novel approach that embeds hyperspectral data, transformed into bilateral probability similarities, onto a nonlinear unit norm coordinate system. By seeking a unit l2-norm nonlinear manifold, we encode similarity representations onto a space in which important regularities in data are easily captured. In its general application, the technique addresses problems related to dimension reduction and visualization of hyperspectral images. Unlike methods such as multidimensional scaling and spherical embeddings, which are based on the notion of pairwise distance computations, our approach is based on a stochastic objective function of spherical coordinates. This allows the use of an Exit probability distribution to discover the nonlinear characteristics that are inherent in hyperspectral data. In addition, the method directly learns the probability distribution over neighboring pixel maps while computing for the optimal embedding coordinates. As part of evaluation, classification experiments were conducted on the manifold spaces for hyperspectral data acquired by multiple sensors at various spatial resolutions over different types of land cover. Various visualization and classification comparisons to five existing techniques demonstrated the strength of the proposed approach while its algorithmic nature is guaranteed to converge to meaningful factors underlying the data. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.relation.ispartofseries Workflow;10594
dc.subject Hyperspectral imagery en_US
dc.subject Algorithms en_US
dc.subject Classification en_US
dc.subject Dimension reduction en_US
dc.subject Embedding en_US
dc.subject Exit distribution en_US
dc.subject Hyperspectral en_US
dc.subject Kullback–Leibler (KL) divergence en_US
dc.subject Unit hyperspherical manifolds en_US
dc.subject Visualization en_US
dc.subject Von Mises–Fisher (vMF) distribution en_US
dc.subject Geophysical image processing en_US
dc.title Spherical stochastic neighbor embedding of hyperspectral data en_US
dc.type Article en_US


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