elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Zhu, Yue und Geiß, Christian und So, Emily (2021) Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification. International Journal of Applied Earth Observation and Geoinformation, 104 (102543), Seiten 1-14. Elsevier. doi: 10.1016/j.jag.2021.102543. ISSN 0303-2434.

[img] PDF - Verlagsversion (veröffentlichte Fassung)
26MB

Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0303243421002506

Kurzfassung

Image super-resolution (SR) techniques can benefit a wide range of applications in the remote sensing (RS) community, including image classification. This issue is particularly relevant for image classification on time series data, considering RS datasets that feature long temporal coverage generally have a limited spatial resolution. Recent advances in deep learning brought new opportunities for enhancing the spatial resolution of historic RS data. Numerous convolutional neural network (CNN)-based methods showed superior performance in terms of developing efficient end-to-end SR models for natural images. However, such models were rarely exploited for promoting image classification based on multispectral RS data. This paper proposes a novel CNNbased framework to enhance the spatial resolution of time series multispectral RS images. Thereby, the proposed SR model employs Residual Channel Attention Networks (RCAN) as a backbone structure, whereas based on this structure the proposed models uniquely integrate tailored channel spatial attention and dense-sampling mechanisms for performance improvement. Subsequently, state-of-the-art CNN-based classifiers are incorporated to produce classification maps based on the enhanced time series data. The experiments proved that the proposed SR model can enable unambiguously better performance compared to RCAN and other (deep learning-based) SR techniques, especially in a domain adaptation context, i.e., leveraging Sentinel-2 images for generating SR Landsat images. Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification.

elib-URL des Eintrags:https://elib.dlr.de/144688/
Dokumentart:Zeitschriftenbeitrag
Titel:Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Zhu, Yueyz591 (at) cam.ac.ukNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Geiß, Christianchristian.geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553NICHT SPEZIFIZIERT
So, Emilyekms2 (at) cam.ac.ukNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:20 September 2021
Erschienen in:International Journal of Applied Earth Observation and Geoinformation
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:104
DOI:10.1016/j.jag.2021.102543
Seitenbereich:Seiten 1-14
Verlag:Elsevier
ISSN:0303-2434
Status:veröffentlicht
Stichwörter:Image super-resolution, Convolutional neural networks, Attention mechanism, Dense connection, Multi-temporal land use 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 - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Geiß, Christian
Hinterlegt am:22 Okt 2021 10:07
Letzte Änderung:04 Dez 2023 12:49

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.