Toward an Unsupervised Colorization Framework for Historical Land Use Classification - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

Toward an Unsupervised Colorization Framework for Historical Land Use Classification

Résumé

We present an unsupervised colorization framework to improve both the visualization and the automatic land use clas- sification of historical aerial images. We introduce a novel algorithm built upon a cyclic generative adversarial neural network and a texture replacement method to homogeneously and automatically colorize unpaired VHR images. We apply our framework on historical aerial images acquired in France between 1970 and 1990. We demonstrate that our approach helps to disentangle hard to classify land use classes and hence improves the overall land use classification.
Fichier principal
Vignette du fichier
ratajczak2019-igarss.pdf (3.77 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02122014 , version 1 (07-05-2019)
hal-02122014 , version 2 (21-10-2019)

Identifiants

  • HAL Id : hal-02122014 , version 1

Citer

Rémi Ratajczak, Carlos F Crispim-Junior, Élodie Faure, Béatrice Fervers, Laure Tougne. Toward an Unsupervised Colorization Framework for Historical Land Use Classification. International Geoscience and Remote Sensing Symposium (IGARSS 2019), IEEE, Jul 2019, Yokohama, Japan. ⟨hal-02122014v1⟩
326 Consultations
176 Téléchargements

Partager

More