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Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

Hong, Danfeng und Gao, Lianru und Yao, Jing und Yokoya, Naoto und Chanussot, Jocelyn und Heiden, Uta und Zhang, Bing (2022) Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing. IEEE Transactions on Neural Networks and Learning Systems, 33 (11), Seiten 6518-6531. IEEE. doi: 10.1109/TNNLS.2021.3082289. ISSN 2162-237X.

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Offizielle URL: https://ieeexplore.ieee.org/document/9444141

Kurzfassung

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Inspired by the powerful learning ability of deep learning (DL), we attempt to develop a general DL approach for HU, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., nonnegativity and sum-to-one) toward a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixelwise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial–spectral unmixing. Experimental results conducted on three different datasets with the ground truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net .

elib-URL des Eintrags:https://elib.dlr.de/189602/
Dokumentart:Zeitschriftenbeitrag
Titel:Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Hong, Danfenghongdf (at) aircas.ac.cnhttps://orcid.org/0000-0002-3212-9584NICHT SPEZIFIZIERT
Gao, LianruChinese Academy of Scienceshttps://orcid.org/0000-0003-3888-8124NICHT SPEZIFIZIERT
Yao, JingChinese Academy of Scienceshttps://orcid.org/0000-0003-1301-9758NICHT SPEZIFIZIERT
Yokoya, NaotoRIKEN Center for Advanced Intelligence Project (AIP)https://orcid.org/0000-0002-7321-4590NICHT SPEZIFIZIERT
Chanussot, JocelynGrenoble Institute of Technologyhttps://orcid.org/0000-0003-4817-2875NICHT SPEZIFIZIERT
Heiden, Utauta.heiden (at) dlr.dehttps://orcid.org/0000-0002-3865-1912NICHT SPEZIFIZIERT
Zhang, BingChinese Academy of Scienceshttps://orcid.org/0000-0001-7311-9844NICHT SPEZIFIZIERT
Datum:2022
Erschienen in:IEEE Transactions on Neural Networks and Learning Systems
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:33
DOI:10.1109/TNNLS.2021.3082289
Seitenbereich:Seiten 6518-6531
Verlag:IEEE
ISSN:2162-237X
Status:veröffentlicht
Stichwörter:Deep Learning Hyperspectral Unmixing Learning Framework
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 - Optische Fernerkundung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Heiden, Dr.rer.nat. Uta
Hinterlegt am:11 Nov 2022 11:19
Letzte Änderung:01 Dez 2022 11:31

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