Optimal Transport for Data Fusion in Remote Sensing

Nicolas Courty (1) , Rémi Flamary (2) , Devis Tuia (3) , Thomas Corpetti (4)

1 OBELIX - Environment observation with complex imagery
2 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
3 LASIG - Laboratoire des Systèmes d'Information Géographique [Lausanne]
4 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences

One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sensing fusion problems: domain adaptation, time series averaging and change detection in LIDAR data.

Mots clés: Optimal transport; domain adaptation; time series analysis; change detection; LIDAR

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]

Nicolas Courty, Rémi Flamary, Devis Tuia, Thomas Corpetti. Optimal Transport for Data Fusion in Remote Sensing. IGARSS, Jul 2016, Beijing, China. ⟨hal-01377234⟩