Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers
Year of publication
2020
Authors
Jacob, Alexander W.; Notarnicola, Claudia; Suresh, Gopika; Antropov, Oleg; Ge, Shaojia; Praks, Jaan; Ban, Yifang; Pottier, Eric; Mallorqui Franquet, Jordi Joan; Duro, Javier; Engdahl, Marcus E.; Vicente-Guijalba, Fernando; Lopez-Martinez, Carlos; Lopez-Sanchez, Juan M.; Litzinger, Marius; Kristen, Harald; Mestre-Quereda, Alejandro; Ziolkowski, Dariusz; Lavalle, Marco
Show moreAbstract
This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain - interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.
Show moreOrganizations and authors
Publication type
Publication format
Article
Parent publication type
Journal
Article type
Original article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A1 Journal article (refereed), original researchPublication channel information
Volume
13
Article number
8966616
Pages
535-552
ISSN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
Yes
Open access of publication channel
Fully open publication channel
License of the publisher’s version
CC BY
Self-archived
Yes
Other information
Fields of science
Geosciences; Electronic, automation and communications engineering, electronics; Materials engineering
Keywords
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Internationality of the publisher
International
Language
English
International co-publication
Yes
Co-publication with a company
Yes
DOI
10.1109/JSTARS.2019.2958847
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes