%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Teillet, Claire
%A Pillot, Benjamin
%A Catry, Thibault
%A Demagistri, Laurent
%A Lyszczarz, D.
%A Lang, M.
%A Couteron, Pierre
%A Barbier, Nicolas
%A Kouassi, A. A.
%A Gunther, Q.
%A Dessay, Nadine
%T Fast unsupervised multi-scale characterization of urban landscapes based on earth observation data
%D 2021
%L fdi:010082187
%G ENG
%J Remote Sensing
%K remote sensing ; multi-scale ; unsupervised ; urban landscapes ; texture
%M ISI:000666681500001
%N 12
%P 2398 [26 ]
%R 10.3390/rs13122398
%U https://www.documentation.ird.fr/hor/fdi:010082187
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-08/010082187.pdf
%V 13
%W Horizon (IRD)
%X 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.
%$ 126 ; 102 ; 020