Toward An Unsupervised Colorization Framework for Historical Land Use Classification
Rémi Ratajczak, C.F. Crispim-Junior, Élodie Faure, Béatrice Fervers, Laure Tougne · 2019
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.