Teillet Claire auteur aut IRD Pillot Benjamin auteur aut IRD Catry Thibault auteur aut IRD Demagistri Laurent auteur aut IRD Lyszczarz D. auteur aut IRD Lang M. auteur aut IRD Couteron Pierre auteur aut IRD Barbier Nicolas auteur aut IRD Kouassi A. A. auteur aut IRD Gunther Q. auteur aut IRD Dessay Nadine auteur aut IRD text journalArticle eng text/pdf reformatted digital access Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the "neighborhood" scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale ("neighbourhoods") and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. specialized remote sensing multi-scale unsupervised urban landscapes texture 126 102 020 13 12 2398 [26 ] 2021 https://www.documentation.ird.fr/hor/fdi:010082187 10.3390/rs13122398 [F B010082187] https://www.documentation.ird.fr/hor/fdi:010082187 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-08/010082187.pdf IRD - Base Horizon / Pleins textes 2021-08-31 2025-02-24 fdi:010082187 fre