Skip to Main content Skip to Navigation
Conference papers

Toward an Unsupervised Colorization Framework for Historical Land Use Classification

Abstract : We present an unsupervised colorization framework to improve both the visualization and the automatic land use classification 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.
Complete list of metadata

Cited literature [6 references]  Display  Hide  Download
Contributor : Rémi Ratajczak Connect in order to contact the contributor
Submitted on : Monday, October 21, 2019 - 8:00:49 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM


Files produced by the author(s)



Rémi Ratajczak, Carlos 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. ⟨10.1109/IGARSS.2019.8898438⟩. ⟨hal-02122014v2⟩



Record views


Files downloads