Hong, Danfeng und Yokoya, Naoto und Chanussot, Jocelyn und Xu, Jian und Zhu, Xiao Xiang (2019) Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-supervised Hyperspectral Dimensionality Reduction. ISPRS Journal of Photogrammetry and Remote Sensing, 158, Seiten 35-49. Elsevier. doi: 10.1016/j.isprsjprs.2019.09.008. ISSN 0924-2716.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0924271619302199?via%3Dihub
Kurzfassung
Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches.
elib-URL des Eintrags: | https://elib.dlr.de/129267/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-supervised Hyperspectral Dimensionality Reduction | ||||||||||||||||||||||||
Autoren: |
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Datum: | Dezember 2019 | ||||||||||||||||||||||||
Erschienen in: | ISPRS Journal of Photogrammetry and Remote Sensing | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
Band: | 158 | ||||||||||||||||||||||||
DOI: | 10.1016/j.isprsjprs.2019.09.008 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 35-49 | ||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||
ISSN: | 0924-2716 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Dimensionality reduction, graph learning, hyperspectral image, iterative, label propagation, multitask regression, remote sensing, semi-supervised. | ||||||||||||||||||||||||
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 Institut für Methodik der Fernerkundung > Atmosphärenprozessoren | ||||||||||||||||||||||||
Hinterlegt von: | Hong, Danfeng | ||||||||||||||||||||||||
Hinterlegt am: | 27 Sep 2019 11:34 | ||||||||||||||||||||||||
Letzte Änderung: | 31 Okt 2023 13:29 |
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