Urban area detection and segmentation using OTB Stéphane May; CNES Jordi Inglada; CNES

1. Urban area detection and segmentation using OTB
Stéphane MAY Jordi INGLADA
stephane.may@cnes.fr jordi.inglada@cnes.fr
orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 1

2. Urban area detection
Context
Floodings due to Hurricane Ike over Gonaives (Haïti) –
September 2009
 Detect urban area to evaluate
the impact zone
Urban area detection applied to:
Rapid mapping
Risk management
Urbanism planning, ...
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 2

3. Urban area detection and segmentation
Contents of the presentation
Introduction
Algorithms description
Parameter estimation and validation process
Analysis of the results
Conclusion
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 3

4. Introduction
Key requirements of the proposed algorithms
Low computation time
Over detection preferred to misdetection
Refined algorithms may be applied in further steps
Algorithm independent of the image resolution
Mixing radiometric techniques (NDVI, NDWI mask) and texture
algorithms
3 algorithms based on texture :
Edge density
Pantex detector
Gabor Texture Index filtering algorithm
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 4

5. Edge density algorithm
Computation of a local density of edges around a pixel
Sobel filter (edge detection)
Application of a threshold
Computation of density of detected edges
One parameter : the filter radius
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 5

6. Pantex detector
Computation of the Haralick contrast texture descriptor
Considering 8 directions, computation of the co-occurence matrixes
Optimal histogram bin defined by the Scott formula
Computation of the Haralick contrast descriptors
The output value of the filter is the min value of contrasts
1 parameter :
radius of the
filter
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 6

7. Gabor Texture Index filtering algorithm (1)
A Gabor filter is a linear filter whose impulse response is defined by a
harmonic function multiplied by a Gaussian function
A=0.10 A=0.12
B=0.20 B=0.12
=0° =45°
f=0.25 f=0.12
Interesting properties to detect textures and alignments
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 7

8. Gabor Texture Index filtering algorithm (2)
Proposed algorithm
Gabor filtering with N directions (Gabor filters bank)
Computation of the local standard deviation with a sliding window
Selection of the median value for the N directions (x M channels)
Hard thresholding to build the binary urban mask
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 8

9. Gabor Texture Index filtering algorithm (3)
Method applicable using each channel of the image or intensity image
Many parameters...
Gabor filter parameters
Number of directions
Radius of the standard deviation filter
 Evaluation of the best parameters set
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 9

10. Urban area detection and segmentation
Contents of the presentation
Introduction
Algorithms description
Parameter estimation and validation process
Analysis of the results
Conclusion
orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 10

11. Parameter estimation and validation process (1)
Use of a Quickbird multispectral image
Creation of a reference mask : urban / non urban
NDVI, and thresholding of clouds, bare soils
Reference mask built for several resolutions : 30m, 20m, 10m,
5m, 2.5m, 1.25m, 0.62m
Monte-Carlo simulation used to evaluate several parameters sets
for the Gabor Texture Index (GTI).
The generated mask is compared to the reference mask :
Computation of True Positive Ratio (TPR), False Positive
Ration (FPR)
Computation of TPR and FPR after application of the NDVI mask
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 11

12. Parameter estimation and validation process (2)
ROC simulations : representation of TPR versus FPR
Indication on processing time (depending of the filters radius)
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 12

13. Parameter estimation and validation process (3)
Selection of interesting parameters for the GTI detector
Robustness to the parameters choice
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 13

14. Urban area detection and segmentation
Contents of the presentation
Introduction
Algorithms description
Parameter estimation and validation process
Analysis of the results
Conclusion
orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 14

15. Analysis of processing time
Comparison of the 3 algorithms
ED and GTI are compliant with preliminary specifications
Pantex filter is not applicable at high resolution
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 15

16. Results – Edge Density
Great improvement thanks to
the NDVI
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 16

17. Results – Gabor Texture Index
Slight performance increase
for the GTI (flat curves for
lower false alarm rates) :
Gabor sensitive to alignments
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 17

18. Results – Pantex filter
Pantex seems to give the
relative worse results in this
experiment but differences
are small
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 18

19. Conclusion
3 interesting algorithms to detect urban areas
Edge density filter : one parameter method, robust and fast
Gabor : slight enhancement of performance among the 3
algorithms (sensitive to alignments)
Pantex : able to detect urban areas, but penalized by the high
computation complexity
Application of the NDVI mask and NDWI enhances the results
Mix of radiometry method and texture method is an excellent
approach
Next step : use algorithms based on geometric features to detect
buildings...
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S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 19

20. OTB contributions
Edge detector and pantex available in a packaged application
otbUrbanAreaExtractionApplication
Gabor Texture Index filter soon available in the OTB
orfeo-toolbox.org
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21. Urban area detection
Thank you for your attention
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