Introduc)on	                                                BPT	  construc)on	                                            ...
Introduc)on	                                              BPT	  construc)on	                                              ...
Introduc8on	                                              BPT	  construc8on	      Hyperspectral	  imagery	                ...
Introduc)on	                                             BPT	  construc)on	           Hyperspectral	  imagery	            ...
Introduc8on	                                                  BPT	  construc8on	      Hyperspectral	  imagery	            ...
Introduc)on	                                            BPT	  construc)on	      Hyperspectral	  imagery	                  ...
Introduc8on	                                            BPT	  construc8on	      Hyperspectral	  imagery	                  ...
Introduc8on	                                            BPT	  construc8on	      Hyperspectral	  imagery	                  ...
Introduc8on	                                               BPT	  construc8on	      Hyperspectral	  imagery	               ...
Introduc8on	                                             BPT	  construc8on	      Hyperspectral	  imagery	                 ...
Introduc8on	                                              BPT	  construc8on	      Region	  Model	                         ...
Introduc8on	                                         BPT	  construc8on	                                         Pruning	  ...
Introduc8on	                                         BPT	  construc8on	                                         Pruning	  ...
Introduc8on	                                         BPT	  construc8on	                                         Pruning	  ...
Introduc8on	                                         BPT	  construc8on	                                         Pruning	  ...
Introduc8on	                                         BPT	  construc8on	                                         Pruning	  ...
Introduc8on	                                               BPT	  construc8on	      Region	  Model	                        ...
Introduc8on	                                    BPT	  construc8on	        Region	  Model	                                 ...
Introduc8on	                                                           BPT	  construc8on	      Region	  Model	            ...
Introduc8on	                                    BPT	  construc8on	        Region	  Model	                                 ...
Introduc8on	                      BPT	  construc8on	        Region	  Model	                      Pruning	  strategy	      ...
Introduc8on	                      BPT	  construc8on	      Region	  Model	                      Pruning	  strategy	     Mer...
Introduc8on	                                BPT	  construc8on	      Region	  Model	                                Pruning...
Introduc8on	                                           BPT	  construc8on	                                           Prunin...
Introduc8on	                                                BPT	  construc8on	                                            ...
Introduc8on	                                              BPT	  construc8on	      Road	  detec)on	                        ...
Introduc8on	                                           BPT	  construc8on	      Hyperspectral	  imagery	                   ...
Introduc8on	                                               BPT	  construc8on	      Road	  detec)on	                       ...
scheme. The strategy is to analyze the BPT using a set of descrip-            with the classical RHSEG [11]. In the case o...
Introduc8on	                                                         BPT	  construc8on	       Road	  detec8on	            ...
Introduc8on	                                                             BPT	  construc8on	      Road	  detec8on	         ...
Introduc8on	                                                          BPT	  construc8on	      Road	  detec)on	            ...
Introduc8on	                                                       BPT	  construc8on	      Road	  detec)on	               ...
Introduc)on	                                                     BPT	  construc)on	                                       ...
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  1. 1. Introduc)on   BPT  construc)on   Pruning  strategy   Conclusions   Improved  BINARY  PARTITION  TREE     construc8on    for  hyperspectral   images:  Applica8on  to  object  detec8on    1,2        2    1    3   S.Valero          ,  P.  Salembier      ,  J.  Chanussot,    C.M.Cuadras    1   GIPSA-­‐Lab,  Signal  &  Image  Dept,  Grenoble-­‐INP,  Grenoble,  France    2   Signal  Theory  and  Comunic.  Dept,  Technical  University  of  Catalonia,  Spain    3   University  of  Barcelona,  Spain  
  2. 2. Introduc)on   BPT  construc)on   Pruning  strategy   Conclusions      Outline   1 Introduc)on   v   Hyperspectral  imagery   v   BPT   2 Binary  Par))on  Tree  Construc)on   v  Region  Model   v   Merging  Criterion     3 Pruning  Strategy   v   Road  detec)on   v   Building  detec)on   4 Conclusions  
  3. 3. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Outline   1 Introduc)on   v   Hyperspectral  imagery   v   BPT   2 Binary  Par))on  Tree  Construc)on   v  Region  Model   v   Merging  Criterion     3 Pruning  Strategy   v   Road  detec)on   v   Building  detec)on   4 Conclusions  
  4. 4. Introduc)on   BPT  construc)on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Hyperspectral  Imagery   v  Different  analysis  techniques  have  been  proposed  in  the  literature.   Most  of  them  process  the  pixels  individually,  as  an  array  of  spectral   data  without  any  spa)al  structure     v  Each  pixel  is  a  discrete   spectrum  containing  the   reflected  solar  radiance  of  the   X Pxy N spa)al  region  that  it   1 represents   Pxy N Radiance 2 1 Wavelength Pixels  are  studied   y as  isolated   discrete  spectra    
  5. 5. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Hyperspectral  Imagery   v  The  ini)al  pixel-­‐based  representa)on  is  a  very  low  level  and              unstructured  representa)on             v  Instead  of  working  with  a  pixel-­‐based  representa)on,  Binary  Par88on   Trees  1  are  an  example  of  a  poweful  structured  region-­‐based  image         representa)on.     2   v  The  construc)on  of  Binary  Par88on  Trees    for  hyperspectral      images  has   been  recently  proposed       1 P.  Salembier  and  L.  Garrido.  Binary  par**on  tree  as  an  efficient  representa*on  for  image   processing,  segmenta*on,  and  informa*on  retrieval.  IEEE  Transac)ons  on  Image  Processing,   vol.9(4),  pp.561-­‐576,  2000.       2 S.Valero,  P.Salembier  and  J.Chanussot.  New  hyperspectral  data  representa)on  using  Binary   Par))on  Tree.  IEEE  Proceedings  of  IGARSS,  2010  
  6. 6. Introduc)on   BPT  construc)on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      BPT   v  BPTs  can  be  interpreted  as  a  structured  image  representa)on  containing   a  set  of  hierarchical  regions  stored  in  a  tree  structure     v  Each  node  represen)ng  a  region  in  the  image,  BPTs  allow  us  to  extract   many  par))ons  at  different  levels  of  resolu)on    
  7. 7. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      BPT   v  BPTs  can  be  interpreted  as  a  structured  image  representa)on  containing   a  set  of  hierarchical  regions  stored  in  a  tree  structure     v  Each  node  represen)ng  a  region  in  the  image,  BPTs  allow  us  to  extract   many  par))ons  at  different  levels  of  resolu)on    
  8. 8. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      BPT   v  BPTs  can  be  interpreted  as  a  structured  image  representa)on  containing   a  set  of  hierarchical  regions  stored  in  a  tree  structure     v  Each  node  represen)ng  a  region  in  the  image,  BPTs  allow  us  to  extract   many  par))ons  at  different  levels  of  resolu)on   ?   How  can  BPT  be   extended  to  the  case  of   hyperspectral  data  ?  
  9. 9. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Aim:  BPT  for  HS  image  analysis  Hyperspectral     Classifica)on   image   CONSTRUCTION     PRUNING     Object  detec)on   Segmenta)on   v  We  propose  to  construct  a  BPT  in  order  to  represent  an  HS  image  with  a  new              region-­‐based  hierarchical  representa)on   Hyperspectral     BPT   image   representa)on  
  10. 10. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Aim:  BPT  for  HS  image  analysis  Hyperspectral     Classifica)on   image   CONSTRUCTION     PRUNING     Object  detec)on   Segmenta)on   v  In  this  paper:  Pruning  strategy  aiming  at  object  detec)on  is  proposed   BPT   Hyperspectral     Search   image   The  pruning  look  for     regions  characterized   by  some  features  
  11. 11. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Outline   1 Introduc)on   v   Hyperspectral  imagery   v   BPT   2 Binary  Par))on  Tree  Construc)on   v  Region  Model   v   Merging  Criterion     3 Pruning  Strategy   v   Road  detec)on   v   Building  detec)on   4 Conclusions  
  12. 12. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Hyperspectral  Imagery   v  The  BPT  is  a  hierarchical  tree   G   structure  represen)ng  an  image   G     v  The  tree  leaves  correspond  to   individual  pixels,  whereas  the  root   E   represents  the  en)re  image   F   F     v  The  remaining  nodes  represent   regions  formed  by  the  merging  of   E   two  children   E   C   D   C   D     v  The  tree  construc)on  is  performed   by  an  itera)ve  region  merging   algorithm   A   B                 B   A   B   C   D  
  13. 13. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Hyperspectral  Imagery   v  The  BPT  is  a  hierarchical  tree   structure  represen)ng  an  image     v  The  tree  leaves  correspond  to   individual  pixels,  whereas  the  root   represents  the  en)re  image     v  The  remaining  nodes  represent   regions  formed  by  the  merging  of   two  children   C   D     v  The  tree  construc)on  is  performed   by  an  itera)ve  region  merging   algorithm   A   B                 B   A   B   C   D  
  14. 14. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Hyperspectral  Imagery   v  The  BPT  is  a  hierarchical  tree   structure  represen)ng  an  image     v  The  tree  leaves  correspond  to   individual  pixels,  whereas  the  root   represents  the  en)re  image     v  The  remaining  nodes  represent   regions  formed  by  the  merging  of   E   two  children   E   C   D   C   D     v  The  tree  construc)on  is  performed   by  an  itera)ve  region  merging   algorithm   A   B                 B   A   B   C   D  
  15. 15. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Hyperspectral  Imagery   v  The  BPT  is  a  hierarchical  tree   structure  represen)ng  an  image     v  The  tree  leaves  correspond  to   individual  pixels,  whereas  the  root   E   represents  the  en)re  image   F   F     v  The  remaining  nodes  represent   regions  formed  by  the  merging  of   E   two  children   E   C   D   C   D     v  The  tree  construc)on  is  performed   by  an  itera)ve  region  merging   algorithm   A   B                 B   A   B   C   D  
  16. 16. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Hyperspectral  Imagery   v  The  BPT  is  a  hierarchical  tree   G   structure  represen)ng  an  image   G     v  The  tree  leaves  correspond  to   individual  pixels,  whereas  the  root   E   represents  the  en)re  image   F   F     v  The  remaining  nodes  represent   regions  formed  by  the  merging  of   E   two  children   E   C   D   C   D     v  The  tree  construc)on  is  performed   by  an  itera)ve  region  merging   algorithm   A   B                 B   A   B   C   D  
  17. 17. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Hyperspectral  Imagery   The  crea)on  of  BPT  implies  two   important  no)ons     Rij   v  Region  model  MRi   It  specifies  how  an  hyperspectral  region   O(Ri,Rj)   is  represented  and  how  to  model  the   O(Ri,Rj)   union  of  two  regions.     Rj   Ri   v  Merging  criterion  O(Ri,Rj)   The  similarity  between  neighboring   regions  determining  the  merging  order  
  18. 18. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Aim:  BPT  for  HS  image  analysis  Hyperspectral     Classifica)on   image   CONSTRUCTION     PRUNING     Object  detec)on   Segmenta)on   v  How  to  represent  hyperspectral   image  regions?   v  Which  similarity  measure  defines  a   good  merging  order?  
  19. 19. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Region  Model   We  propose  a  non-­‐parametric   sta)s)cal  region  model  2   Radiance   Pixel  1   consis)ng  in  a  set  of  N  probability  density   Pixel  2   func)ons   Pixel  3   A         B   where  each  Pi  represents  the   probabili)y  that  the  spectra  data   Wavelength  λ   set  has  a  specific  radiance  value   λi   in  the  wavelength  λi     Hλi   2   F.  Calderero  and  F.  Marques.Region-­‐Merging   techniques  using  informa*on  theory  sta*s*cal   measures.  IEEE  Transac)ons  on  Image   Processing,  vol.19,  pp.1567-­‐1586,  2010.   B   A   Nbins  
  20. 20. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Aim:  BPT  for  HS  image  analysis  Hyperspectral     Classifica)on   image   CONSTRUCTION     PRUNING     Object  detec)on   Segmenta)on   v  How  to  represent  hyperspectral  image   regions?   v  Which  similarity  measure  defines  a   good  merging  order?  
  21. 21. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Merging  Criterion   Step  1   Step  2   Principal  Coordinates   Mul8dimensional   Scaling   Associa8on   Measure   Mul8dimensional   Scaling   Principal  Coordinates  
  22. 22. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Merging  Criterion   Step  1   Mul8dimensional   Principal  Coordinates   Scaling   Analyze  the  inter-­‐waveband   similarity  rela)onships  for  each   data  via  metric  scaling  to  obtain   the  principal  coordinates   Mul8dimensional   Scaling   Principal  Coordinates  
  23. 23. Introduc8on   BPT  construc8on   Region  Model   Pruning  strategy   Merging  Criterion   Conclusions      Merging  Criterion:  Step  2   Step  2   Principal  Coordinates   PC1   An  associa)on  measure   Mul8dimensional   is  defined  by  considering   Scaling   that  PC1  and  PC2  form  a   Associa8on   mul)variate  regression   Measure   model     Mul8dimensional   Scaling   Principal  Coordinates   PC2     PC1=    PC2  β      +e     Are  Regression  Coefficients   equal  to  0  ???  
  24. 24. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Rosis  Hyperspectral  data   Data  Set  :  Rosis  Pavia  Center  103  bands     RGB  Composi)on   103  bands   BPT  is  constructed  by  using  the   proposed  merging  order   Nregions=22   Nregions=32   Nregions=42  
  25. 25. Introduc8on   BPT  construc8on   Pruning  strategy   Conclusions      Rosis  Hyperspectral  data  Ground  truth  manually  created    A  symmetric  distance  for  object  evalua)on:  It  is   defined  as  the  he  minimum  number  of  pixels  whose   labels  should  be  changed    to  achieve  perfect   matching,  normalized  by  the  total  number  of  pixels   of  the  image  minus  one   NRegions=22   NRegions=22   Symmetric  distance   Symmetric  distance   to  ground  truth     to  ground  truth     equal  to  0.48   equal  to  0.227   [Tilton,  2005]   RHSEG  soiware   Hierarchical  BPT  level  
  26. 26. Introduc8on   BPT  construc8on   Road  detec)on   Pruning  strategy   Building  detec)on   Conclusions      Outline   1 Introduc)on   v   Hyperspectral  imagery   v   BPT   2 Binary  Par))on  Tree  Construc)on   v  Region  Model   v   Merging  Criterion     3 Pruning  Strategy   v   Road  detec)on   v   Building  detec)on   4 Conclusions  
  27. 27. Introduc8on   BPT  construc8on   Hyperspectral  imagery   Pruning  strategy   BPT   Conclusions      Aim:  BPT  for  HS  image  analysis  Hyperspectral     Classifica)on   image   CONSTRUCTION     PRUNING     Object  detec)on   Segmenta)on   v  Pruning  strategy  aiming  at  object  detec)on  is  proposed   BPT   Hyperspectral     Search   image   The  pruning  look  for     regions  characterized   by  some  features  
  28. 28. Introduc8on   BPT  construc8on   Road  detec)on   Pruning  strategy   Building  detec)on   Conclusions      Object  Detec8on  Strategy   v  Hyperspectral  object  detec)on  has  been  mainly  developed  in  the              context  of  pixel-­‐wise  spectral  classifica)on.     v  Objets  are  not  only  characterized  by  their  spectral  signature.       v  Spa)al  features  such  as  shape,  area,  orienta)on  can  also  contribute   significantly  to  the  detec)on.   v  Roads  appear  as  elongated  structures  having  fairly  homogeneous  radiance   values  usually  corresponding  to  asphalt.   Besides  the  informa8on  provided   by  asphalt  spectrum,  a  road   contains  important  spa8al  features    
  29. 29. scheme. The strategy is to analyze the BPT using a set of descrip- with the classical RHSEG [11]. In the case of R scheme. The strategy is to analyze the BPT using a set of descrip- ity criterion used is SAM with spectral clustering w Introduc8on   forfor each node. The work presented here proposes the tors computed tors computed each node. The work presented here proposes the ity criterion used is SAM with spectral clustering evaluate the resulting partitions, the symmetric di analysis of three different descriptors for each node: evaluate the resulting partitions, the symmetric BPT  construc8on   different descriptors for each node: analysis of three Road  detec)on   is used as a partition quality evaluation. Having a is used as a partition quality evaluation. Having Pruning  strategy  D = {Dshape , Dspectral , Darea } ground truth GT , the symmetric distance corresp Building  detec)on   D = {Dshape , Dspectral , Darea } (7) (7) ground truth GT , the symmetric distance corre mum number of pixels whose labels should be cha Conclusions   mum number of pixels whose labels should be The proposed shape, spectral and area descriptors are related P to achieve a perfect matching with GT , norma The proposed shape, spectral and area descriptors are related P to achieve a perfect matching with GT , nor to the specficic object of interest. Studying D from the leaves to number of pixels in the image. The manually crea to the specficic object of interest. Studying D from the leaves to number of pixels in the image. The manually c    Object  Detec8on  Strategy   the root, the approach consists in removing all nodes that signifi- in Fig. 4(b). the root, the approach consists in removing all nodes that signifi- in Fig. 4(b). cantly differ from the characterization proposed by a reference Dref . Fig. 4(c)(d) show the segmentation results obtain cantly differ from the characterization proposed by a reference Dref . Fig. 4(c)(d) show the segmentation results obta Hence, given this reference, the idea consists in considering that the RHSEG, respectively. In both cases, the resulting Hence, given this reference, the idea consists in considering that the RHSEG, respectively. In both cases, the resultin searched object instances are defined by the closest nodes to the root 63 regions. Comparing both results, the quantiti searched object instances are defined by the closest nodes to the root 63 regions. Comparing both results, the quan node that have descriptors close to the Dref . model. In order to visual evaluation corroborates that the partition ex node that have descriptors close to the Dref . model. In order to visual evaluation corroborates that the partition illustrate the generality of the approach, we describe two detection BPT are much closer to the ground truth than the on illustrate the generality of the approach, we describe two detection BPT are much closer to the ground truth than the examples: roads and building in urban scenes. RHSEG. This experiment has been done with sev examples: roads and building in urban scenes. RHSEG. This experiment has been done with s acquired by different hyperspectral sensors, but, d acquired by different hyperspectral sensors, but tations, we can only present one data set. Howeve 3.1. Detection of roads 3.1. Detection of roads We  analyse  each   tations, we can only present one data set. Howe were the same on the remaining dataset. were the same on the remaining dataset. Roads appear as elongated structures having fairly homogeneous ra- A second set of experiments are conducted no Roads appear as elongated structures having fairly homogeneous ra- diance values usually corresponding to asphalt. Given their charac- BPT  to  look  for  the   A second set of experiments are conducted tection and recognition. We compare the classica v  Firstly,  the  spa)al  and  the  spectral   diance values usually corresponding to asphalt. Given their charac- teristic shape, Dshape is the elongation of the region. In order to tection and recognition. We compare the class sification against the strategy proposed in section 3 compute it, we first define the smallest rectangular bounding box descriptors  are  computed  for  all   compute it, we first define the smallest rectangular bounding box nodes  having   teristic shape, Dshape is the elongation of the region. In order to sification against the strategy proposed in sectio road detection. The pixel-wise classification con road detection. The pixel-wise classification c specific  spa8al  and     BPT  nodes   spectral  descriptors         v  Secondly,    following  a  bojom-­‐up   strategy,  the  pruning  select  nodes   closer  to  the  root  which  have  a   low  elonga)on,    a  high  correla)on   between  asphalt  and  an  area   higher  than  a  threshold   Star8ng  from  the  leaves  
  30. 30. Introduc8on   BPT  construc8on   Road  detec8on   Pruning  strategy   Building  detec)on   Conclusions      Object  Detec8on:  Example  of  Roads     v  Roads  appear  as   The  ra)o  between  the  height  and  the  width  of  the  minimum elongated  structures   bounding  box  containing  the  region                 Oriented  Bounding  Box   containing  the  region   height   width  
  31. 31. Introduc8on   BPT  construc8on   Road  detec8on   Pruning  strategy   Building  detec)on   Conclusions      Object  Detec8on:  Example  of  Roads   v  Roads  have  fairly   Correla)on  coefficient  between  the  mean  spectra  of  the   homogeneous  radiance   region  and  the  asphalt  reference  spectrum     values  usually     corresponding  to   asphalt                 Mean   Correla)on   Spectrum   Coefficient   Pixel  1   Asphalt  reference  spectrum   Radiance   Pixel  2   Pixel  3   Wavelength  λ  
  32. 32. Introduc8on   BPT  construc8on   Road  detec)on   Pruning  strategy   Building  detec)on   Conclusions      Object  Detec8on:  Example  of  Roads   Par88ons  contained  in  BPT   Hydice  Hyperspectral    image   27  regions   37  regions   57  regions     Pixel-­‐wise  Asphalt  detec)on   BPT  pruning  strategy   (Spectra  whose  Correla)on   oriented  to  object   with  asphalt  is  higher  than  0.9)   detec)on      
  33. 33. Introduc8on   BPT  construc8on   Road  detec)on   Pruning  strategy   Building  detec)on   Conclusions      Object  Detec8on:  Example  of  Building   Par88ons  contained  in  BPT   Hydice  Hyperspectral    image   27  regions   37  regions   57  regions     Pixel-­‐wise  Building  detec)on   BPT  pruning  strategy   (Spectra  whose  Correla)on  with   oriented  to  object   reference  is  higher  than  0.9)   detec)on      
  34. 34. Introduc)on   BPT  construc)on   Pruning  strategy   Conclusions      Conclusions     v  A  new  region  merging  algorithm  has  been  presented  here  to  construct  BPTs  as  a   hyperspectral  region-­‐based  and  hierarchical  representa)on.   v  Being  a  generic  representa)on,  many  tree  processing  techniques  can              be  formulated  as  pruning  strategies  for  many  applica)ons.  Here,  as  an                    example  of  BPT  processing,  a  pruning  strategy  has  been  proposed  for  object                      detec)on.     v  A  new  similarity  measure  for  merging  hyperspectral  regions  have  been  proposed   taking  into  account  spa)al  and  spectral  correla)ons.  It  introduces  a  local  dimension   reduc)on  which  is  different  from  the  classical  point  of  view  where  the  reduc)on  is   applied  globally  on  the  en)re  image.   v  The  processing  example  has  shown  the  advantage  of  using  region-­‐based   representa)ons.    
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