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CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

Hong, Danfeng and Yokoya, Naoto and Chanussot, Jocelyn and Zhu, Xiao Xiang (2019) CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences. IEEE Transactions on Geoscience and Remote Sensing, 57 (7), pp. 4349-4359. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2890705 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/document/8672122

Abstract

With a large amount of open satellite multispectral imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global multispectral land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-multispectral (HS-MS) correspondences. The multispectral out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HS-MS datasets (University of Houston and Chikusei), where HS-MS data sets have trade-offs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/122307/
Document Type:Article
Title:CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Chanussot, JocelynInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:March 2019
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:57
DOI :10.1109/TGRS.2018.2890705
Page Range:pp. 4349-4359
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:common subspace learning, cross-modality learning, hyperspectral, landcover classification, multispectral, remote sensing.
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 13:34
Last Modified:04 Dec 2019 17:11

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