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Local Manifold Learning with Robust Neighbors Selection for Hyperspectral Dimensionality Reduction

Hong, Danfeng and Yokoya, Naoto and Zhu, Xiao Xiang (2016) Local Manifold Learning with Robust Neighbors Selection for Hyperspectral Dimensionality Reduction. In: Proceedings of IGARSS 2016, pp. 40-43. IEEE Xplore. IGARSS 2016, 10-15 July 2016, Beijing, China. doi: 10.1109/IGARSS.2016.7729001. ISSN 2153-7003 (E).

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Official URL: http://ieeexplore.ieee.org/document/7729001/


Manifold learning has been successfully applied to hyperspectral dimensionality reduction to embed nonlinear and nonconvex manifolds in the data. However, dimensionality reduction by manifold learning is sensitive to non-uniform data distribution and the selection of neighbors. To address the two issues to some extents, in this work a new manifold framework based on locality linear embedding (LLE), namely local normalization and local feature selection (LNLFS), is proposed. Classification is explored as a potential application to validate the proposed algorithm. Classification accuracy using data obtained using different dimensionality reduction methods is evaluated and compared, while applying two kinds of strategies for selecting the training and test samples: random sampling and region-based sampling. Experimental results show the classification accuracy obtained with LNLFS is superior to state-of-the-art dimensionality reduction methods.

Item URL in elib:https://elib.dlr.de/109187/
Document Type:Conference or Workshop Item (Lecture)
Title:Local Manifold Learning with Robust Neighbors Selection for Hyperspectral Dimensionality Reduction
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Yokoya, Naotonaoto.yokoya (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:January 2016
Journal or Publication Title:Proceedings of IGARSS 2016
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IGARSS.2016.7729001
Page Range:pp. 40-43
EditorsEmailEditor's ORCID iD
Publisher:IEEE Xplore
ISSN:2153-7003 (E)
Keywords:hyperspectral image, dimensionality reduction, manifold learning, local normalization, local feature selection, non-uniform data distribution
Event Title:IGARSS 2016
Event Location:Beijing, China
Event Type:international Conference
Event Dates:10-15 July 2016
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Hong, Danfeng
Deposited On:08 Dec 2016 08:26
Last Modified:31 Jul 2019 20:06

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