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Spatial-Spectral Manifold Embedding of Hyperspectral Data

Hong, Danfeng und Yao, Jing und Wu, Xin und Chanussot, Jocelyn und Zhu, Xiao Xiang (2020) Spatial-Spectral Manifold Embedding of Hyperspectral Data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII, Seiten 1-6. 24th ISPRS Congress, 2020-08-31 - 2020-09-02, Nice, France. doi: 10.5194/isprs-archives-xliii-b3-2020-423-2020. ISSN 1682-1750.

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Offizielle URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/423/2020/

Kurzfassung

In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatialspectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods.

elib-URL des Eintrags:https://elib.dlr.de/135533/
Dokumentart:Konferenzbeitrag (Poster)
Titel:Spatial-Spectral Manifold Embedding of Hyperspectral Data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Hong, DanfengDanfeng.Hong (at) dlr.dehttps://orcid.org/0000-0002-3212-9584NICHT SPEZIFIZIERT
Yao, JingJing.Yao (at) dlr.dehttps://orcid.org/0000-0003-1301-9758NICHT SPEZIFIZIERT
Wu, XinBeijing Institute of TechnologyNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Chanussot, Jocelynjocelyn (at) hi.ishttps://orcid.org/0000-0003-4817-2875NICHT SPEZIFIZIERT
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613NICHT SPEZIFIZIERT
Datum:September 2020
Erschienen in:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
Band:XLIII
DOI:10.5194/isprs-archives-xliii-b3-2020-423-2020
Seitenbereich:Seiten 1-6
ISSN:1682-1750
Status:veröffentlicht
Stichwörter:hyperspectral, embedding, spatialspectral manifold embedding, patch-based, classification
Veranstaltungstitel:24th ISPRS Congress
Veranstaltungsort:Nice, France
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:31 August 2020
Veranstaltungsende:2 September 2020
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben hochauflösende Fernerkundungsverfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Yao, Jing
Hinterlegt am:20 Jul 2020 12:50
Letzte Änderung:24 Apr 2024 20:38

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