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Learning A Common Subspace from Hyperspectral-Multispectral Correspondences

Hong, Danfeng and Yokoya, Naoto and Zhu, Xiao Xiang and Chanussot, Jocelyn (2018) Learning A Common Subspace from Hyperspectral-Multispectral Correspondences. WHISPERS 2018, 23.-26. September 2018, Amsterdam, Netherlands.

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Official URL: http://www.ieee-whispers.com/2017/11/23/whispers-2018/

Abstract

With a large amount of multispectral imagery available (e.g. Sentinel-2, Landsat-8), considerable attention has been paid to global multispectral landcover classification. There is, however, a typical bottleneck for further improving the performance of classification in the poor spectral information of multispectral data. On the contrary, hyperspectral data fails to be largely collected but is characterized by rich spectral information. To this end, we aim to learn a common subspace from hyperspectral-multispectral correspondences by simultaneously considering subspace learning and classification. Local manifold structure jointly constructed from different modalities is further embedded into the proposed framework. With the learned projections, the multispectral out-of-samples can be smoothly projected into the common subspace, which are expected to be better clarified. Extensive experiments on two HS-MS datasets where MS data sets are theoretically generated by their corresponding HS data, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with several state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/122308/
Document Type:Conference or Workshop Item (Poster)
Title:Learning A Common Subspace from Hyperspectral-Multispectral Correspondences
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Chanussot, JocelynInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Date:2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Cross-modality learning, common subspace learning, hyperspectral, landcover classification, multispectral, remote sensing.
Event Title:WHISPERS 2018
Event Location:Amsterdam, Netherlands
Event Type:international Conference
Event Dates:23.-26. September 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hong, Danfeng
Deposited On:19 Oct 2018 13:39
Last Modified:30 Nov 2018 11:26

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