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Deep Learning for Land Cover Change Detection

Sefrin, Oliver 1; Riese, Felix M. 1; Keller, Sina 1
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)

Abstract:
Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000128034
Veröffentlicht am 04.01.2021
Originalveröffentlichung
DOI: 10.3390/rs13010078
Scopus
Zitationen: 16
Web of Science
Zitationen: 16
Dimensions
Zitationen: 18
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
Karlsruher Institut für Technologie (KIT)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000128034
Erschienen in Remote sensing
Verlag MDPI
Band 13
Heft 1
Seiten Article no: 78
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 28.12.2020
Schlagwörter machine learning; multi-class classification; long short-term memory network (LSTM); fully convolutional neural network (FCN); multitemporal; time series; Sentinel-2
Nachgewiesen in Web of Science
Dimensions
Scopus
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