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Multitemporal classification without new labels: A solution with optimal transport


Tuia, Devis; Flamary, Rémi; Rakotomamonjy, Alain; Courty, Nicolas (2015). Multitemporal classification without new labels: A solution with optimal transport. In: 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015, Annecy (F), 22 July 2015 - 24 July 2015, 1-4.

Abstract

Re-using models trained on a specific image acqui- sition to classify landcover in another image is no easy task. Illumination effects, specific angular configurations, abrupt and simple seasonal changes make that the spectra observed, even though representing the same kind of surface, drift in a way that prevents a non-adapted model to perform well. In this paper we propose a relative normalization technique to perform domain adaptation, i.e. to make the data distribution in the images more similar before classification. We study optimal transport as a way to match the image-specific distributions and propose two regularization schemes, one unsupervised and one semi-supervised, to obtain more robust and semantic matchings. Code is available at http://remi.flamary.com/soft/soft-transp.html. Experiments on a challenging triplet of WorldView2 images, comparing three neighborhoods of the city of Zurich at different time instants, confirm the effectiveness of the proposed method that can perform adaptation in these non-coregistered and very different urban case studies.

Abstract

Re-using models trained on a specific image acqui- sition to classify landcover in another image is no easy task. Illumination effects, specific angular configurations, abrupt and simple seasonal changes make that the spectra observed, even though representing the same kind of surface, drift in a way that prevents a non-adapted model to perform well. In this paper we propose a relative normalization technique to perform domain adaptation, i.e. to make the data distribution in the images more similar before classification. We study optimal transport as a way to match the image-specific distributions and propose two regularization schemes, one unsupervised and one semi-supervised, to obtain more robust and semantic matchings. Code is available at http://remi.flamary.com/soft/soft-transp.html. Experiments on a challenging triplet of WorldView2 images, comparing three neighborhoods of the city of Zurich at different time instants, confirm the effectiveness of the proposed method that can perform adaptation in these non-coregistered and very different urban case studies.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:24 July 2015
Deposited On:14 Jan 2016 10:32
Last Modified:18 Aug 2017 22:06
Publisher:IEEE
ISBN:978-1-4673-7119-3
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/Multi-Temp.2015.7245773
Official URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7245773

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