Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data
<p>Study areas and satellite imagery used in this study. On the left, band 2 (blue) of Sentinel 2 images on Bouake (<b>top</b>) and Brasilia (<b>bottom</b>) are shown (Copernicus Sentinel data (2018-2020)/ESA). On the right Pleiades images (panchromatic channel) on the northeastern part of Bouake and Sao Sebastiao, a satellite City of Brasilia (Contains information © CNES 2020, Distribution Airbus DS, all rights reserved. No commercial use.).</p> "> Figure 2
<p>Potential of FOTOTEX for the multi-scale characterization of urban landscapes, with the scales of analysis and associated Earth observation data.</p> "> Figure 3
<p>Simplified methodological framework of the FOTOTEX algorithm, divided in four steps (<b>a</b>) image partitioning, (<b>b</b>) spectral analysis by Fourier transform and R-spectra computation, (<b>c</b>) Principal Component Analysis and (<b>d</b>) RGB composite of the three main components (textural indices). These steps will be described in detail in the method section below.</p> "> Figure 4
<p>Influence of spectral bands used as input in the FOTOTEX algorithm for the characterization of urban areas at the macro-scale using Sentinel 2 images on Bouake. (<b>a</b>) B2 (blue) sentinel 2 band, and (<b>b</b>) B6 (NIR) Sentinel 2 band. (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 5
<p>Influence of the window size parameter in the FOTOTEX algorithm for the characterization of urban areas at the macro-scale using Sentinel 2 images on Bouake and Brasilia. (<b>a</b>) B2 (blue) sentinel 2 band on Bouake with a window size of 5 pixels, (<b>b</b>) B2 (blue) sentinel 2 band on Bouake with a window size of 31 pixels, (<b>c</b>) B2 (blue) sentinel 2 band on Brasilia with a window size of 5 pixels and (<b>d</b>) B2 (blue) sentinel 2 band on Brasilia with a window size of 31 pixels. (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 6
<p>Influence of the dc component and the normalization parameters in the FOTOTEX algorithm for the characterization of urban areas at the macro-scale using Sentinel 2 images on Bouake. (<b>a</b>) B2 (blue) sentinel 2 band on Bouake with DC component (DC = True) and without normalization (N = False), (<b>b</b>) B2 (blue) sentinel 2 band on Bouake without DC component (DC = False) and without normalization (N = False) and (<b>c</b>) B2 (blue) sentinel 2 band on Bouake with DC component (DC = True) and normalization (N = True). The text in red highlights the modified parameter compared with image (<b>a</b>). (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 7
<p>Comparison of urban footprints extracted from Sentinel 2 images on Bouake and Brasilia with FOTOTEX with the Global Human Settlement Layer (GHSL) product from JRC (© European Union) over different window sizes, (<b>a</b>) urban footprints on Bouake window size = 5 pixels, (<b>b</b>) urban footprints on Bouake window size = 31 pixels, (<b>c</b>) urban footprints on Brasilia window size = 5 pixels, (<b>d</b>) urban footprints on Brasilia window size = 31 pixels, Sentinel 2 Background © ESA (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 8
<p>Influence of various parameters (partitioning method, pixel size and window size) in FOTOTEX in the characterization of urban units at the meso-scale from Pleiades images on the northeastern part of Bouake and Sao Sebastiao, Brasilia. (<b>a</b>,<b>e</b>) use of block method, (<b>b</b>,<b>f</b>) use of moving window method, (<b>c</b>,<b>g</b>) use of a 3 m input pixel size over, (<b>d</b>,<b>h</b>) use of a window size of 31 pixels. The text in red highlights the modified parameter compared with image (<b>a</b>). (PX = pixel size, WS = window size, PM = partitioning method, MW = Moving Window, DC = DC component, N = normalize).</p> "> Figure 9
<p>Influence of DC component and normalization in FOTOTEX in the characterization of urban units at the meso-scale from Pleiades images on the northeastern part of Bouake and Sao Sebastiao, Brasilia. (<b>a</b>,<b>d</b>) use of block method over Bouake and Brasilia, with DC component, without normalization, (<b>b</b>,<b>e</b>) use of block method over Bouake and Brasilia, without DC component, without normalization, (<b>c</b>,<b>f</b>) use of block method over Bouake and Brasilia, with DC component and normalization. The text in red highlights the modified parameter compared with image (<b>a</b>). (PX = pixel size, WS = window size, PM = partitioning method, MW = Moving Window, DC = DC component, N = normalize).</p> "> Figure 10
<p>Study of the relationship between texture information at the meso-scale extracted using FOTOTEX on Pleiades images on Sao Sebastiao, Brasilia (<b>a</b>) and the northeastern part of Bouake (<b>c</b>), with the density of buildings represented as heatmaps using building contours from the SEDUH database (<a href="http://www.seduh.df.gov.br/" target="_blank">http://www.seduh.df.gov.br/</a> accessed on 20 September 2020) for Brasilia (<b>b</b>) and OpenStreetMap for Bouake (<b>d</b>). On this figure, black/colored lines and numbers are urban units extracted from the combination of texture and contours from the urban footprint produced with FOTOTEX and Sentinel 2.</p> "> Figure 11
<p>Influence of the partitioning method for the micro-scale characterization of urban objects on Bouake and Brasilia using Pleiades images (<b>a</b>) block method on Bouake, (<b>b</b>) block method on Brasilia, (<b>c</b>) moving window method on Bouake and (<b>d</b>) moving window method on Brasilia (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 12
<p>Implementation of the FOTOTEX algorithm on (<b>a</b>) a UAV image captured over the city of Bouake (<b>b</b>) showing the RGB composite produced by FOTOTEX (<b>c</b>) and the three Principal Components. (PX = pixel size, WS = window size, PM = partitioning method, DC = DC component, N = normalize).</p> "> Figure 13
<p>Extraction of buildings edges with (<b>a</b>) a segmentation method carried out with the Large Scale Generic Region Merging algorithm (Orfeo Toolbox) and (<b>b</b>) the FOTOTEX method implemented on the blue band of UAV image. (<b>c</b>,<b>d</b>) zooms on two different areas to compare detection by FOTOTEX and segmentation.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Satellite Imagery
2.2. Method
- the urban footprint (macro-scale)
- the urban units scale (meso-scale)
- the object scale such as buildings (micro-scale)
2.3. FOTOTEX Algorithm
- Step 1: Image partitioning (Figure 3a)
- Step 2: Spectral analysis by Fourier transform and R-spectra computing (Figure 3b)
- Step 3: Texture ordination (Figure 3c,d)
2.4. Influence of Technical Parameters
3. Results
3.1. Macro-Scale: Urban Footprint
3.1.1. Influence of Spectral Bands as Input
3.1.2. Influence of Partitioning Method
3.1.3. Influence of Window Size
3.1.4. Influence of Keeping the DC Component
3.1.5. Influence of the Normalization
3.1.6. Extraction of the Urban Footprint over Optimal Configuration
3.2. Meso-Scale: Urban Units
3.2.1. Influence of Partitioning Method
3.2.2. Influence of the Pixel Size
3.2.3. Influence of Window Size
3.2.4. Influence of DC Component and Normalization
3.2.5. Comparison of Urban Units Extracted from FOTOTEX with Environmental Variables
3.3. Micro-Scale: Buildings Detection
3.3.1. Influence of Parameters
3.3.2. Extraction of Urban Objects
4. Discussion
4.1. Recommended Parameters for the Study of the Urban Landscapes at Three Spatial Scales
4.1.1. Macro-Scale
4.1.2. Meso-Scale
4.1.3. Micro-Scale
4.1.4. Global Recommendation
4.2. The Contribution of the Method to Characterize Urban Landscapes at Three Scales
- we provide an open algorithm implementing a fully unsupervised procedure to characterize urban areas at three scales. This is particularly important to work on areas where training data are missing.
- for all the analysis scales, our methodological framework relies on single date images, strongly reducing the need for downloading and storing large volumes of satellite images
- using single images, the computational power required to run the algorithm is strongly reduced.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Choice |
---|---|
Spectral band | Depends on the satellite image |
Partitioning method (PM) | “Block” or “Moving window” |
Pixel size (PX) | Any |
Window size (in pixel) (WS) | Any positive odd number |
Keep DC component (DC) | True or False |
Normalize (N) | Normalize or non-normalized |
Bouake (Scene of 2801 × 2751 Pixels) | Brasilia (Scene of 9306 × 6192 Pixels) | |
---|---|---|
Block Method | 25 s | 2 min |
Moving window method | 1 min | 9 min |
Moving window method (HDF5 and incremental PCA) | 3 min | 15 min |
FOTOTEX | GHSL | GUF | |||||||
---|---|---|---|---|---|---|---|---|---|
Input Information | MW WS = 5 (10 m) | Block WS = 5 (50 m) | Block WS = 5 (310 m) | 10 m | 50 m | 310 m | 10 m | 50 m | 310 m |
Surface of the urban foorptint of Brasilia in km | 457.2 | 453.8 | 663.3 | 338.9 | 338.5 | 342.5 | 385.2 | 385.6 | 391.8 |
Surface of the urban foorptint of Bouake in km | 106.4 | 106.7 | 157.2 | 34.7 | 34.7 | 34.9 | 38.5 | 38.5 | 39.5 |
Sao Sebastiao | Urban Units | Mean PC1 | Mean PC2 | Mean PC3 | Percentage of Built Area | Percentage of Vegetated Area |
1 | 7.13 | −0.09 | 0.80 | 14.84 | 36.19 | |
2 | 3.98 | 0.13 | 0.54 | 32.20 | 29.37 | |
3 | 3.62 | 0.37 | 0.00 | 38.87 | 33.51 | |
4 | 3.85 | 0.18 | 0.06 | 18.35 | 56.07 | |
5 | 3.79 | 0.89 | 0.20 | 10.30 | 59.69 | |
Northeastern part of Bouake | Urban Units | Mean PC1 | Mean PC2 | Mean PC3 | Percentage of Built Area | Percentage of Vegetated Area |
1 | 6.24 | −0.34 | 0.00 | 10.31 | 70.17 | |
2 | 8.44 | −0.37 | −0.09 | 7.64 | 76.55 | |
3 | 5.47 | 0.02 | −0.52 | 13.15 | 70.49 | |
4 | 6.29 | −0.48 | −0.27 | 12.00 | 72.52 | |
5 | 6.54 | −2.59 | −1.63 | 44.97 | 39.89 |
Parameters | Macro Scale | Meso scale | Micro scale |
---|---|---|---|
Partitioning Method (PM) | Block | Block | Block |
Pixel Size (PX) | 10 m | Between 1 m and 3 m | Under 1 m |
Window Size (in pixel) (WS) | To be adapted | To be adapted (better result with larger window) | To be adapted |
Keep DC Component (DC) | True | True | True |
Normalize (N) | Non-normalized | Non-normalized | Non-normalized |
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Teillet, C.; Pillot, B.; Catry, T.; Demagistri, L.; Lyszczarz, D.; Lang, M.; Couteron, P.; Barbier, N.; Adou Kouassi, A.; Gunther, Q.; et al. Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data. Remote Sens. 2021, 13, 2398. https://doi.org/10.3390/rs13122398
Teillet C, Pillot B, Catry T, Demagistri L, Lyszczarz D, Lang M, Couteron P, Barbier N, Adou Kouassi A, Gunther Q, et al. Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data. Remote Sensing. 2021; 13(12):2398. https://doi.org/10.3390/rs13122398
Chicago/Turabian StyleTeillet, Claire, Benjamin Pillot, Thibault Catry, Laurent Demagistri, Dominique Lyszczarz, Marc Lang, Pierre Couteron, Nicolas Barbier, Arsène Adou Kouassi, Quentin Gunther, and et al. 2021. "Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data" Remote Sensing 13, no. 12: 2398. https://doi.org/10.3390/rs13122398