Abstract

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.

Highlights

  • Urban landscapes are composed of objects of various sizes and materials arranged by humans in a complex manner [1]

  • The results presented in this paper propose a first attempt to fill this gap, introducing the use of a single texture-based analysis for the multi-scale characterization of urban areas

  • Some authors point out that even if no global dataset is available, future applications on remote sensing over urban areas have to be based on methodologies that can be applied globally on any urban areas [7,42,43].The methodological framework we propose aims at describing urban areas at three scales to characterize urban footprint, urban units or neighbourhoods and buildings

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Summary

Introduction

Urban landscapes are composed of objects of various sizes and materials arranged by humans in a complex manner [1]. Urban areas are defined as the footprint of impervious surfaces (buildings and roads), including pervious surfaces such as vegetation within the footprint [2,3].It is the built environment in which population is distributed. Urban areas are characterized by a multi-scale structure. Urban analysis using Earth Observation (EO) data is scale dependent [4]. Current optical and SAR sensors offer a wide range of spatial, temporal and spectral resolutions. Medium and high resolution (10–500 m) images (MODIS, Landsat, Sentinel) can provide information on the urban footprint and delineate basic urban classes, while very high resolution (∼1 m) data . .) provide more precise information at a finer scale to delineate individual buildings and even estimate volumes [5].

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