72 citations · 287 across the 33 of their papers we have counts for
5 papers · 1 filter
Distance transform regression for spatially-aware deep semantic segmentation
Nicolas Audebert, Alexandre Boulch, Bertrand Le Saux +1
Understanding visual scenes relies more and more on dense pixel-wise classification obtained via deep fully convolutional neural networks. However, due to the nature of the network…
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks. Especially, we investigate generative adversarial networks and their applic…
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolution…
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
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…
On the usability of deep networks for object-based image analysis
Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
As computer vision before, remote sensing has been radically changed by the introduction of Convolution Neural Networks. Land cover use, object detection and scene understanding in…