Fully Convolutional Neural Networks For Remote Sensing Image Classification

Emmanuel Maggiori 1, * Yuliya Tarabalka 1 Guillaume Charpiat 2 Pierre Alliez 1
* Auteur correspondant
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.
Type de document :
Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. IEEE, pp.5071-5074, Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01350706
Contributeur : Emmanuel Maggiori <>
Soumis le : lundi 1 août 2016 - 14:06:54
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : mardi 8 novembre 2016 - 19:13:40

Fichier

Maggiori_IGARSS2016.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01350706, version 1

Citation

Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. Fully Convolutional Neural Networks For Remote Sensing Image Classification. IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. IEEE, pp.5071-5074, Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 〈hal-01350706〉

Partager

Métriques

Consultations de la notice

1117

Téléchargements de fichiers

2819