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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
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, ...


                                                                                    orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                            2
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                         3
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


                                                                               orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                       4
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




                                                                      orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                              5
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




                                                                                    orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                            6
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




                                                                              orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                      7
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




                                                                               orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                       8
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




                                                                          orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                  9
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
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

                                                                           orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                  11
Parameter estimation and validation process (2)

       ROC simulations : representation of TPR versus FPR
              Indication on processing time (depending of the filters radius)




                                                                          orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                 12
Parameter estimation and validation process (3)

       Selection of interesting parameters for the GTI detector




       Robustness to the parameters choice




                                                                  orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                         13
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
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



                                                                          orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                 15
Results – Edge Density




                                                               Great improvement thanks to
                                                               the NDVI




                                                                                orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                       16
Results – Gabor Texture Index




                                                           Slight performance increase
                                                           for the GTI (flat curves for
                                                           lower false alarm rates) :
                                                           Gabor sensitive to alignments



                                                                              orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                     17
Results – Pantex filter




                                                                   Pantex seems to give the
                                                                   relative worse results in this
                                                                   experiment but differences
                                                                   are small


                                                                                       orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                              18
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...

                                                                        orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                               19
OTB contributions

       Edge detector and pantex available in a packaged application




                                                                 otbUrbanAreaExtractionApplication



       Gabor Texture Index filter soon available in the OTB
                                                                                          orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                                 20
Urban area detection




                                             Thank you for your attention




                                                                            orfeo-toolbox.org
S. MAY – J. INGLADA – Urban area detection – IGARSS 2009                                   21

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Urban area detection and segmentation using OTB

  • 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, ... orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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) orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 15
  • 16. Results – Edge Density Great improvement thanks to the NDVI orfeo-toolbox.org 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 orfeo-toolbox.org 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 orfeo-toolbox.org 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... orfeo-toolbox.org 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 S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 20
  • 21. Urban area detection Thank you for your attention orfeo-toolbox.org S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 21