Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data
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