BLIND SOURCE SEPARATION OF HYPERSPECTRAL DATA IN PLANETARY REMOTE SENSING: ENDMEMBER EXTRACTION AND VALIDATION
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BLIND SOURCE SEPARATION OF HYPERSPECTRAL DATA IN PLANETARY REMOTE SENSING: ENDMEMBER EXTRACTION AND VALIDATION

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BLIND SOURCE SEPARATION OF HYPERSPECTRAL DATA IN PLANETARY REMOTE SENSING: ENDMEMBER EXTRACTION AND VALIDATION BLIND SOURCE SEPARATION OF HYPERSPECTRAL DATA IN PLANETARY REMOTE SENSING: ENDMEMBER EXTRACTION AND VALIDATION Presentation Transcript

  • ACCES au laboratoire GIPSA-lab Feuille de routeX.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   1   GIPSA-lab
  • Mars  observed  by  Viking  Orbiters  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   2  
  • Mars  observed  by  Viking  Orbiters   Geographical  linear  mixture   Mineral   dust   Pixel  size  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   3  
  • Mars  observed  by  Viking  Orbiters   Geographical  linear  mixture   Mineral   dust   Pixel  size  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   4  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   5  
  • HiRISE@MRO  (snapshot   HiRISE@MRO  25  cm/pix)   THEMIS  ~100  m/pix   40  km   20  km  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   6  
  • HiRISE@MRO  (snapshot   HiRISE@MRO  25  cm/pix)   THEMIS  ~100  m/pix  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   7  
  • HiRISE@MRO  (25  cm/pix)   CRISM  pixel     footprint  Mars  Reconnaissance  Orbiter  CRISM   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   8  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   9  
  • Raw  image   Artifact  cleaning   Photometric  correction   Atmospheric  correction   Clean  image  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   10  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   11  
  • BPSS endmember spectra BPSS Dark source 0.25 0.5 0.4 0.2 REFF value 0.3 0.2 0.15 0.1 A 0.1 B 0 e1 e2 e3 e4 e5 e6 e5’ 0 50 100 150 200 250 0 50 100 150 200 250 0.5 BPSS Strong bright source 0.4 mn : spectral  signature  of   BPSS Weak bright source 0.4 BPSS endmember 1 0.35 endmember  n BPSS endmember 2 0.3 REFF value 0.3 0.5 0.45 0.25 0.2 0.4 0.2 0.4 0.1 C 0.35 0.15 D 0.3 0.1 e1’ e2’ e3’ e4’ e6’ 0.3 0 0.25 0.05 0 50 100 150 200 250 0 50 100 150 200 250 CRISM spectral band 0.2 CRISM spectral band 0.2 0.15 0.1 0.1 0.05 BPSS endmember 4 sn : abundance  map  of   BPSS endmember 5 0.6 endmember  n   0.8 0.5 0.7 0.4 0.6 0.5 0.3 0.4 0.2 0.3 0.2 0.1 0.1X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   12  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   13  
  • VCA   MVC-­‐NMF   spatial-­‐VCA   BPSS   [Nascimento’05]   [Miao’07]   [Zortea’09]   [Moussaoui’06]   Geometric  method   First   Minimum  volume   Incorporation  of   Statistical   with  pure  pixel   principles:   constraint   spatial  information   approach   assumption   -­‐  Fast  &  efficient   -­‐  Bayesian   -­‐  Less-­‐prevalent   -­‐  Homogeneous  Advantages:   -­‐  Endmembers  are   endmembers   endmembers   framework   physical   -­‐  Error  bars   -­‐  Non-­‐physical   -­‐  Spatially-­‐ -­‐  Impact  of  noise   spectra   -­‐  Non-­‐physical   confined  and  less-­‐Drawbacks:   -­‐  Less-­‐prevalent   spectra   prevalent   -­‐  High   endmembers   computational   endmembers   time   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   14  
  • VCA   MVC-­‐NMF   spatial-­‐VCA   BPSS   [Nascimento’05]   [Miao’07]   [Zortea’09]   [Moussaoui’06]   Geometric  method   First   Minimum  volume   Incorporation  of   Statistical   with  pure  pixel   principles:   constraint   spatial  information   approach   assumption   -­‐  Fast  &  efficient   -­‐  Bayesian   -­‐  Less-­‐prevalent   -­‐  Homogeneous  Advantages:   -­‐  Endmembers  are   endmembers   endmembers   framework                           physical   -­‐  Error  bars   -­‐  Non-­‐physical   -­‐  Spatially-­‐ -­‐  Impact  of  noise   spectra   -­‐  Non-­‐physical   confined  and  less-­‐Drawbacks:   -­‐  Less-­‐prevalent   spectra   prevalent   -­‐  High   endmembers   computational   endmembers   time   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   15  
  • VCA   MVC-­‐NMF   spatial-­‐VCA   BPSS   [Nascimento’05]   [Miao’07]   [Zortea’09]   [Moussaoui’06]   Geometric  method   First   Minimum  volume   Incorporation  of   Statistical   with  pure  pixel   principles:   constraint   spatial  information   approach   assumption   -­‐  Fast  &  efficient   -­‐  Bayesian   -­‐  Less-­‐prevalent   -­‐  Homogeneous  Advantages:   -­‐  Endmembers  are   endmembers   endmembers   framework                           physical   -­‐  Error  bars   -­‐  Non-­‐physical   -­‐  Spatially-­‐ -­‐  Impact  of  noise   spectra   -­‐  Non-­‐physical   confined  and  less-­‐Drawbacks:   -­‐  Less-­‐prevalent   spectra   prevalent   -­‐  High   endmembers   computational   endmembers   time   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   16  
  • VCA   MVC-­‐NMF   spatial-­‐VCA   BPSS   [Nascimento’05]   [Miao’07]   [Zortea’09]   [Moussaoui’06]   Geometric  method   First   Minimum  volume   Incorporation  of   Statistical   with  pure  pixel   principles:   constraint   spatial  information   approach   assumption   -­‐  Fast  &  efficient   -­‐  Bayesian   -­‐  Less-­‐prevalent   -­‐  Homogeneous  Advantages:   -­‐  Endmembers  are   endmembers   endmembers   framework                           physical   -­‐  Error  bars   -­‐  Non-­‐physical   -­‐  Spatially-­‐ -­‐  Impact  of  noise   spectra   -­‐  Non-­‐physical   confined  and  less-­‐Drawbacks:   -­‐  Less-­‐prevalent   spectra   prevalent   -­‐  High   endmembers   computational   endmembers   time   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   17  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   18  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   19  
  • 0.6 Apparent reflectance 0.4 0.5 0.4 0.3 0.3 0.2 0.2 0.1 A! B!Spectral  product:  spectral  signatures   1 2 3 4 5 6 0 1 Spatial  product:  abundance  maps   2 3 4 5 6 1.32 1.65 1.98 2.31 2.64 1.32 1.65 1.98 2.31 2.64 MVC NMF associated spectra spatial VCA associated spectra R1.1  um   B2.3  um   0.4 0.4Apparent reflectance 0.3 0.3 A! B! 0.2 0.2 0.1 0.1 C! 1.32 1 2 1.65 3 4 1.98 5 2.31 6 2.64 D! 1.32 1 2 1.65 3 4 1.98 5 2.31 6 2.64 Wavelength in microns Wavelength in microns C! D! Dark   Strong  bright   Weak  bright   Final  product:  composite   abundance  map!   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   20  
  • Sources:  Dark,  strong  bright,  weak  bright   C! HiRISE  image   [Ceamanos  TGRS  2011]   MVC-­‐NMF  composite  map  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   21  
  • Sources:  Dark,  strong  bright,  weak  bright   VCA   BPSS   A! B! MVC-­‐NMF   spatial-­‐VCA   C! D!X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   22  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   23  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   24  
  • HiRISE@MRO  (25  cm/pix)  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   25  
  • Dark  features   reference   abundance   map CO2  ice   CRISM HiRISE 150 m 50 m Detail  of  the  Russell  dune  observed  by  the  CRISM  and  the  HiRISE   instruments.  CRISM  frt42aa  in  blue,  HiRISE  PSP_002482_1255  in  green  X.  Ceamanos.  26/07/11     IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   26  
  • Dark  features   reference   abundance   map A! CO2  ice   C! CRISM HiRISE 150 m 50 m Detail  of  the  Russell  dune  observed  by  the  CRISM  and  the  HiRISE   instruments.  CRISM  frt42aa  in  blue,  HiRISE  PSP_002482_1255  in  green  X.  Ceamanos.  26/07/11     IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   27  
  • HiRISE PSP_002482_1255 CRISM frt000042aa Registration correlation coefficient 1 0.25  m/pix   18  m/pix   Avg.  Corr.  =   0.9 29862×63004  pix   604×420  pix   0.7   0.8 0.7 0.6 HiRISE  image   0.5 0.4 0.3 1.  Registration   CRISM  image   0.2 0.1 A! B! C! 0 Reg.  HiRISE  image   2.  Classification   Classification  map   3.  Pixel  counting   Abundance  map   (ground  truth)  X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   28  
  • HiRISE PSP_002482_1255 CRISM frt000042aa Registration correlation coefficient 1 0.25  m/pix   18  m/pix   Avg.  Corr.  =   0.9 29862×63004  pix   604×420  pix   0.7   0.8 0.7 0.6 HiRISE  image   0.5 0.4 0.3 1.  Registration   CRISM  image   0.2 0.1 A! B! C! 0 Reg.  HiRISE  image   2.  Classification   Classification  map   Classification map Ground truth 1 A! B! 0.9 0.8 3.  Pixel  counting   0.7 0.6 0.5 Abundance  map   0.4 (ground  truth)   0.3 0.2 0.1 0X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   29  
  • HiRISE PSP_002482_1255 CRISM frt000042aa Registration correlation coefficient 1 0.25  m/pix   18  m/pix   Avg.  Corr.  =   0.9 29862×63004  pix   604×420  pix   0.7   0.8 0.7 0.6 HiRISE  image   0.5 0.4 0.3 1.  Registration   CRISM  image   0.2 0.1 A! B! C! 0 Reg.  HiRISE  image   2.  Classification   Classification  map   Classification map Ground truth 1 Pixel  counting  for  two  CRISM  pixels   A! B! 0.9 0.8 3.  Pixel  counting   a(xi)=0.10   a(xj)=0.35   0.7 0.6 0.5 Abundance  map   0.4 (ground  truth)   0.3 0.2 0.1 0X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   30  
  • VCA   BPSS   MVC-­‐NMF   spatial-­‐VCA  Ground  truth   Registration  accuracy   •  10%  error  between  ground  truth  and  unmixing  results   •  MVC-­‐NMF  obtains  the  best  r =  0.83  and  ε =  0.08   •  BPSS  provides  accurate  abundances   •  VCA  provides  underestimated  abundances   •  spatial-­‐VCA  does  not  extract  the  dark  source  satisfactorily X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   31  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   32  
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   33  
  • ACCES au laboratoire GIPSA-lab Feuille de routeX.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   34   GIPSA-lab
  • X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   35  
  • Dark  Strong  bright  Weak  bright   Non-­‐linear  residue   due  to  unaccurate   atmospheric  correction   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   36  
  • VZA=-­‐30º   Dark  Strong  bright  Weak  bright   target   Non-­‐linear  residue   due  to  unaccurate   atmospheric  correction   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   37  
  • VZA=30º   VZA=-­‐30º   Dark  Strong  bright  Weak  bright   target   Non-­‐linear  residue   due  to  unaccurate   atmospheric  correction   X.  Ceamanos.  26/07/11   IGARSS  2011  -­‐  xavier.ceamanos@obs.ujf-­‐grenoble.fr   38