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

Nicolas Audebert; Bertrand Le Saux; Sébastien Lefèvre

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arXiv · 1701.05818

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

Nicolas Audebert·Bertrand Le Saux·Sébastien Lefèvre

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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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Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. 2017-01-20. Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper). https://arxiv.org/abs/1701.05818

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