Hong, Danfeng und Hu, Jingliang und Yao, Jing und Chanussot, Jocelyn und Zhu, Xiao Xiang (2021) Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model. ISPRS Journal of Photogrammetry and Remote Sensing, 178, Seiten 68-80. Elsevier. doi: 10.1016/j.isprsjprs.2021.05.011. ISSN 0924-2716.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0924271621001362
Kurzfassung
As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively, particularly for heterogeneous data sources. Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e., Houston2013 – hyperspectral and multispectral data, Berlin – hyperspectral and synthetic aperture radar (SAR) data, Augsburg – hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification. Extensive experiments conducted on the three datasets demonstrate the superiority and advancement of our S2FL model in the task of land cover classification in comparison with previously-proposed state-of-the-art baselines. Furthermore, the baseline codes and datasets used in this paper will be made available freely at https://github.com/danfenghong/ISPRSS2FL.
elib-URL des Eintrags: | https://elib.dlr.de/146206/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model | ||||||||||||||||||||||||
Autoren: |
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Datum: | 12 Juni 2021 | ||||||||||||||||||||||||
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: | 178 | ||||||||||||||||||||||||
DOI: | 10.1016/j.isprsjprs.2021.05.011 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 68-80 | ||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||
ISSN: | 0924-2716 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Benchmark datasets, Classification, Feature learning, Hyperspectral, Land cover mapping, DSM, Multimodal, Multispectral, Remote sensing, SAR, Shared features, Specific features, HySpex | ||||||||||||||||||||||||
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 - Künstliche Intelligenz | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||
Hinterlegt von: | Rösel, Dr. Anja | ||||||||||||||||||||||||
Hinterlegt am: | 26 Nov 2021 09:26 | ||||||||||||||||||||||||
Letzte Änderung: | 04 Dez 2023 12:45 |
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