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Open AccessArticle

GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes

1
Université Bretagne Sud—IRISA UMR 6074, 56 000 Vannes, France
2
SIRS, 59 650 Villeneuve-d’Ascq, France
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 46; https://doi.org/10.3390/ijgi8010046
Received: 10 August 2018 / Revised: 13 December 2018 / Accepted: 24 December 2018 / Published: 18 January 2019
(This article belongs to the Special Issue GEOBIA in a Changing World)
Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data. View Full-Text
Keywords: big data; scalability; multiscale analysis; land cover mapping; woody feature mapping; differential attribute profiles; random forest; open source big data; scalability; multiscale analysis; land cover mapping; woody feature mapping; differential attribute profiles; random forest; open source
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Merciol, F.; Faucqueur, L.; Damodaran, B.B.; Rémy, P.-Y.; Desclée, B.; Dazin, F.; Lefèvre, S.; Masse, A.; Sannier, C. GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes. ISPRS Int. J. Geo-Inf. 2019, 8, 46.

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