Chaudhuri, Ushashi und Dey, Subhadip und Datcu, Mihai und Banerjee, Biplab und Bhattacharya, Avik (2021) Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, Seiten 9884-9898. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2021.3112209. ISSN 1939-1404.
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Offizielle URL: https://ieeexplore.ieee.org/document/9537619
Kurzfassung
Conventional remote sensing data analysis techniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This article exploits the contextual information capturing ability of deep neural networks, particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to nonlinear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multispectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multilabeled images and use it to design an interband group retrieval framework. We demonstrate its effectiveness over the conventional triplet-loss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.
elib-URL des Eintrags: | https://elib.dlr.de/144955/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive | ||||||||||||||||||||||||
Autoren: |
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Datum: | 27 Oktober 2021 | ||||||||||||||||||||||||
Erschienen in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
Band: | 14 | ||||||||||||||||||||||||
DOI: | 10.1109/JSTARS.2021.3112209 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 9884-9898 | ||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||
ISSN: | 1939-1404 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Interband retrieval, multilabel classification,multilabel cross triplet loss, multimodal classification, Sentinel-2,land-cover classification | ||||||||||||||||||||||||
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: | Otgonbaatar, Soronzonbold | ||||||||||||||||||||||||
Hinterlegt am: | 18 Nov 2021 12:39 | ||||||||||||||||||||||||
Letzte Änderung: | 25 Nov 2021 13:50 |
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