Toward an Unsupervised Colorization Framework for Historical Land Use Classification

Rémi Ratajczak (1) , Carlos F Crispim-Junior (1) , Élodie Faure (2) , Béatrice Fervers (2) , Laure Tougne (1)

1 imagine - Extraction de Caractéristiques et Identification
2 Département cancer environnement (Centre Léon Bérard - Lyon)

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.

Mots clés: GAN; Texture filters; Colorization; Index Terms-Colorization; Deep Learning; Historical Land Use; Classification; Land Use

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Environnement et Société

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⟩