Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)

Nicolas Audebert 1, 2 Bertrand Le Saux 1 Sébastien Lefèvre 2
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.
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https://hal.archives-ouvertes.fr/hal-01438499
Contributor : Nicolas Audebert <>
Submitted on : Tuesday, January 17, 2017 - 5:40:33 PM
Last modification on : Tuesday, March 26, 2019 - 2:24:45 PM
Document(s) archivé(s) le : Tuesday, April 18, 2017 - 3:30:17 PM

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  • HAL Id : hal-01438499, version 1
  • ARXIV : 1701.05818

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Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper). Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. ⟨hal-01438499⟩

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