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Unsupervised classification and spectral unmixing for sub-pixel labelling


           A.Villa   , ,† ,   J.Chanussot , J.A. Benediktsson , C.Jutten

            GIPSA-lab, Signal & Image Dept., Grenoble Institute of Technology, France.
           Faculty of Electrical and Computer Engineering, University of Iceland, Iceland.
                                †
                                  Aresys, Politecnico di Milano, Italy.




                                 IEEE IGARSS 2011
                          Vancouver, Canada - 2011
A new approach to classification                        Experiments                                  Conclusions

 Hyperspectral Images


         Widely used in remote sensing:


         λ
                                                                        √
                                                                            Wide spectral range and large
                                                                            number of wavelengths

                                             - Trees
                                             - Grass


                                                                        √
                                                                            Very high spectral resolution


                                      VIS         NIR
                                    0.4 μm       2.4 μm                 × Tradeoff between spectral and
                                                                          spatial resolution




         Jocelyn Chanussot                                  Gipsa-Lab                                  2 / 21
A new approach to classification                     Experiments                                  Conclusions

 Challenges


         Low spatial resolution → appearance of mixed pixels


                                                            • Common in hyperspectral images


                                    Pure pixel:             • Traditional classifiers inadequate,
                                    100% grass
                                                                  partially addressed by mixed pixel
                                                                  techniques


                                  Mixed pixel:              • Critical for land cover mapping
                                  70% metal sheet
                                  30% grass




         Joint use (full + mixed techniques) desirable, but little investigated
         [Wang and Jia, 2010].



         Jocelyn Chanussot                               Gipsa-Lab                                  3 / 21
A new approach to classification                         Experiments                                  Conclusions

 Challenges


         Low spatial resolution → appearance of mixed pixels


                                                                • Common in hyperspectral images


                                        Pure pixel:             • Traditional classifiers inadequate,
                                        100% grass
                                                                      partially addressed by mixed pixel
                                                                      techniques


                                      Mixed pixel:              • Critical for land cover mapping
                                      70% metal sheet
                                      30% grass




         Incorporation of spectral unmixing in the classification process:
             • Does it provide accuracy improvement?
             • Is it possible to improve the classification map spatial resolution?




         Jocelyn Chanussot                                   Gipsa-Lab                                  3 / 21
A new approach to classification                Experiments      Conclusions




        1    A new approach to classification



        2    Experiments



        3    Conclusions




         Jocelyn Chanussot                          Gipsa-Lab      4 / 21
A new approach to classification                                Experiments                                       Conclusions

 Context


         Traditional techniques neglect sub-pixel and spatial information



         Additional information provided by unmixing not fully exploited

                                                                                     0.6


                                                                             0.9

                                    Pure pixel:
                                    100% grass                                  1    0.9   0.8


                                                                             0.6      1


                                  Mixed pixel:                                        1     1    0.8
                                  70% metal sheet
                                  30% grass
                                                                             0.9     0.6    1



                Original image                      Classification                   Unmixing           Finer resolution?

         How to jointly use full and mixed pixel techniques?



         Jocelyn Chanussot                                          Gipsa-Lab                                       5 / 21
A new approach to classification                                   Experiments                Conclusions

 Proposed Approach


                                                      Low resolution
                                                    hyperpspectral data




                                                       Unmixing
                                     Classes                                    Abundances
                                  identification                                   maps



                                                    Classification

                                                        "Upsampled"
                                                     classification map




                                                   Spatial regularization




                                                        Final map




         Jocelyn Chanussot                                              Gipsa-Lab               6 / 21
A new approach to classification                                         Experiments                                        Conclusions

 Proposed Approach

    1. Abundances fractions are computed from a HSI


                                                                            Step 1:
                                     Low resolution
                                   hyperpspectral data



                                                                                                                   Pure pixel:
                                                                                                                   100% grass
            Step 1


                                        Step 2                                                                   Mixed pixel:
                   Classes                                 Abundances                                            70% metal sheet
                identification                                maps                                               30% grass




                                                                            Step 2:
                                       "Upsampled"                                             0.6
                                    classification map


                                                                                         0.9


                                                                                         1     0.9   0.8
                                  Spatial regularization


                                                                                         0.6    1


                                                                                                1     1    0.8
                                       Final map


                                                                                         0.9   0.6    1



         Jocelyn Chanussot                                                   Gipsa-Lab                                           7 / 21
A new approach to classification              Experiments                          Conclusions

 The proposed approach



                     M = Mixed pixel

                                  M         Proposed method

     M                                M     The abundances computation is in two
                                            steps, to take the spatial information into
                                            account:
                                             1. Pixels with an abundance over a
                                                certain threshold are considered ’pure’
                                      M M
                                             2. Abundances of ’mixed’ pixels are
                 M                    M         computed by selecting as endmembers
                                                pixels spatially close
                 M


         Jocelyn Chanussot                        Gipsa-Lab                          8 / 21
A new approach to classification         Experiments                          Conclusions

 The proposed approach



                     M = Mixed pixel

                                       Proposed method
                                       The abundances computation is in two
                                       steps, to take the spatial information into
                                       account:
                                        1. Pixels with an abundance over a
                                           certain threshold are considered ’pure’

                                        2. Abundances of ’mixed’ pixels are
                                           computed by selecting as endmembers
                                           pixels spatially close




         Jocelyn Chanussot                   Gipsa-Lab                          8 / 21
A new approach to classification                                          Experiments                              Conclusions

 Proposed Approach


    2. Creation of a finer classification map


                                                                         Step 2:
                                                                                                0.6
                                     Low resolution
                                   hyperpspectral data
                                                                                          0.9


                                                                                          1     0.9   0.8


                                                                                          0.6    1

                                        Step 2                                                   1     1    0.8
                    Classes                                Abundances
                 identification                               maps
                                                                                          0.9   0.6    1


                                                                Step 3
                                       "Upsampled"
                                                                         Step 3:
                                    classification map




                                  Spatial regularization




                                       Final map




         Jocelyn Chanussot                                                    Gipsa-Lab                              9 / 21
A new approach to classification                                              Experiments      Conclusions

 Proposed Approach

    3. Final spatial regularization


                                                                             Step 3:
                                     Low resolution
                                   hyperpspectral data




                    Classes                                  Abundances
                 identification                                 maps




                                                                    Step 3
                                       "Upsampled"
                                                                             Step 4:
                                    classification map


                                                           Step 4

                                  Spatial regularization




                                       Final map




         Jocelyn Chanussot                                                        Gipsa-Lab      10 / 21
A new approach to classification                          Experiments                                   Conclusions

 Spatial regularization



         Criterion: minimization of the total perimeter of the connected areas (e.g.,
         belonging to the same class)


                                     M
                                   0,5 0,3
                                     0,2                                           M
                                                                                 0,5 0,3
                                                                                   0,2
                 0,7
                  M
                 0,3                         M
                                             0,6
                                             0,4
                                                                  0,7
                                                                   M
                                                                  0,3                      M
                                                                                           0,6
                                                                                           0,4



                                             0,9
                                             0,8   0,9
                                                   0,6                                     0,9
                                                                                           0,8   0,9
                                                                                                 0,6
                                             M 0,4
                                               M
                                             0,1
                                               0,1
                                             0,2                                           M 0,4
                                                                                             M
                                                                                           0,1
                                                                                             0,1
                                                                                           0,2
                             0,9             0,9                           0,9             0,9
                             M
                             0,1             M
                                             0,7
                                             0,1
                                             0,3                           M
                                                                           0,1             M
                                                                                           0,7
                                                                                           0,1
                                                                                           0,3

                             M
                             0,9
                             0,1                                           M
                                                                           0,9
                                                                           0,1

                    Criterion not satisfied                                Criterion satisfied




         Jocelyn Chanussot                                    Gipsa-Lab                                   11 / 21
A new approach to classification                  Experiments                                    Conclusions




       Spectral unmixing based approach [Villa            Novelties introduced:
       et al., 2010]

                                                               1. Retrieve classes with unsupervised
          1. VCA for class retrieval                              clustering
                                                                  (→ more robust to outliers)
          2. FCLS for abundance determination
                                                               2. Include spatial information
                                                                  (→ use more accurate
          3. Simulated Annealing for spatial
                                                                  endmembers)
             regularization




         Jocelyn Chanussot                            Gipsa-Lab                                    12 / 21
A new approach to classification                                        Experiments                                              Conclusions

 VCA vs. K-MEANS


        6000                                                            6000

        5000                                                            5000

        4000                                                            4000

        3000                                                            3000


        2000                                                            2000


        1000                                                            1000


              0                                                             0
       4000                                                             4000


          2000                                                             2000


                                                                6000                                              4000   5000    6000
                  0                 2000   3000   4000   5000                        0       1000   2000   3000
                      0      1000                                                        0




                                      VCA                                                    K-MEANS



         Jocelyn Chanussot                                                  Gipsa-Lab                                              13 / 21
A new approach to classification                Experiments      Conclusions




        1    A new approach to classification



        2    Experiments



        3    Conclusions




         Jocelyn Chanussot                          Gipsa-Lab      14 / 21
A new approach to classification                              Experiments                 Conclusions

 How to verify the results?




                                                 Decrease original
                                                    resolution




                              Final map
                                                                              Proposed
                         (sub-pixel precision)
                                                                              approach




         Jocelyn Chanussot                                        Gipsa-Lab                 15 / 21
A new approach to classification                    Experiments                              Conclusions

 Experiments on real data


       ROSIS University data set                   AISA data set

           • Classification of a metal sheet roof       • 400×500 pixels area, six classes of
               (120×90 pixels)                            interest
           • 1.3 m spatial resolution, 103             • 6 m spatial resolution, 252 spectral
               spectral bands.                            bands
           • Spatial resolution of the original        • Spatial resolution of the original data
               data degraded of a factor 3                degraded of a factor 5




         Jocelyn Chanussot                              Gipsa-Lab                               16 / 21
A new approach to classification                      Experiments                Conclusions

 Real data sets



         ROSIS data set:




        Original Image            K-means (93.75%)   VCA+SU (96.95%)   KM+SU (95.89%)




         Jocelyn Chanussot                                Gipsa-Lab                17 / 21
A new approach to classification           Experiments                 Conclusions

 Real data set

         AISA data set:




           K-means (51.61%)       VCA+SU (59.69%)          KM+SU (75.72%)

         Jocelyn Chanussot                     Gipsa-Lab                 18 / 21
A new approach to classification                Experiments      Conclusions




        1    A new approach to classification



        2    Experiments



        3    Conclusions




         Jocelyn Chanussot                          Gipsa-Lab      19 / 21
A new approach to classification                        Experiments                     Conclusions

 Conclusions and Perspectives




         New method to improve spatial resolution of thematic maps:
             • Unsupervised clustering to define classes
             • Integration of spatial information to locally model abundances
             • Simulated Annealing proposed for spatial regularization




         Clustering less sensitive to extreme pixels, VCA better in highly mixed scenarios



         Next step: Incorporate spectral variability of the classes




         Jocelyn Chanussot                                  Gipsa-Lab                     20 / 21
A new approach to classification                                         Experiments                             Conclusions




          Unsupervised classification and spectral unmixing for sub-pixel labelling


                                  A.Villa   , ,† ,   J.Chanussot , J.A. Benediktsson , C.Jutten

                               GIPSA-lab, Signal & Image Dept., Grenoble Institute of Technology, France.
                              Faculty of Electrical and Computer Engineering, University of Iceland, Iceland.
                                                   †
                                                     Aresys, Politecnico di Milano, Italy.




                                                        IEEE IGARSS 2011
                                                 Vancouver, Canada - 2011




         Jocelyn Chanussot                                                   Gipsa-Lab                             21 / 21
A new approach to classification                              Experiments                                  Conclusions

 Challenges


         Hyperspectral images issues:
             1   Curse of dimensionality
             2   Exploitation of contextual information
             3   Presence of mixed pixels



                                                                           • Common in hyperspectral images
                                             Pure pixel:
                                             100% grass
                                                                           • Traditional classifiers inadequate



                                           Mixed pixel:
                                                                           • Usually not considered for
                                           70% metal sheet                    classification!
                                           30% grass




         Jocelyn Chanussot                                        Gipsa-Lab                                  22 / 21
A new approach to classification                          Experiments                     Conclusions

 Context



         Traditional approaches to image analysis are full pixel and mixed pixel techniques
             • Full pixel techniques are traditional classification algorithms
             • Mixed pixel techniques are spectral unmixing, soft classification, . . .




         Joint use is desirable, but little investigated [Wang and Jia, 2010].


         Incorporation of spectral unmixing in the classification process:
             • Does it provide accuracy improvement?
             • Is it possible to improve the classification map spatial resolution?




         Jocelyn Chanussot                                    Gipsa-Lab                     23 / 21
A new approach to classification                      Experiments                           Conclusions

 Linear Spectral Unmixing

         Abundances estimation through spectral unmixing:
             • Goal: find extreme pixels (endmembers) that can be used to "unmix" other pixels.
             • Each "mixed" pixel is a combination of endmember fractional abundances.




         Jocelyn Chanussot                                Gipsa-Lab                           24 / 21
A new approach to classification                      Experiments                           Conclusions

 Linear Spectral Unmixing

         Abundances estimation through spectral unmixing:
             • Goal: find extreme pixels (endmembers) that can be used to "unmix" other pixels.
             • Each "mixed" pixel is a combination of endmember fractional abundances.




         Jocelyn Chanussot                                Gipsa-Lab                           24 / 21
A new approach to classification                                Experiments                                       Conclusions

 Context



         Traditional techniques neglect information

         Additional information provided by unmixing not fully exploited

                                                                                     0.6


                                                                             0.9

                                    Pure pixel:
                                    100% grass                                  1    0.9   0.8


                                                                             0.6      1


                                  Mixed pixel:                                        1     1    0.8
                                  70% metal sheet
                                  30% grass
                                                                             0.9     0.6    1



                Original image                      Classification                   Unmixing           Finer resolution?

         How to jointly use full and mixed pixel techniques?




         Jocelyn Chanussot                                          Gipsa-Lab                                       25 / 21
A new approach to classification                                      Experiments                                        Conclusions

 The proposed approach


                     M = Mixed pixel
                                                                   Proposed method
                                  M                                We propose a technique in four steps:

     M                                M                              1. Preliminary classification with
                                                                        probabilistic classifier (SVM)

                                                                     2. Choose suitable endmember
                                                                        candidates and perform unmixing
                                      M M                            3. Split every pixel into n sub-pixels, and
                                                                        assign them to a class
                 M                    M
                                                                     4. Perform spatial regularization in order
                 M                                                      to correctly locate sub-pixels


          A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images
  at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011


         Jocelyn Chanussot                                                Gipsa-Lab                                        26 / 21
A new approach to classification                                      Experiments                                        Conclusions

 The proposed approach


                     M = Mixed pixel
                                                                   Proposed method
                                                                   We propose a technique in four steps:
                                                                     1. Preliminary classification with
                                                                        probabilistic classifier (SVM)

                                                                     2. Choose suitable endmember
                                                                        candidates and perform unmixing

                                                                     3. Split every pixel into n sub-pixels, and
                                                                        assign them to a class

                                                                     4. Perform spatial regularization in order
                                                                        to correctly locate sub-pixels


          A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images
  at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011


         Jocelyn Chanussot                                                Gipsa-Lab                                        26 / 21
A new approach to classification                                      Experiments                                        Conclusions

 The proposed approach



                                                                   Proposed method
                          0,5 0,3
                            0,2   M                                We propose a technique in four steps:
    0,7
                                      M
                                      0,6                            1. Preliminary classification with
     M
    0,3                               0,4                               probabilistic classifier (SVM)

                                                                     2. Choose suitable endmember
                                                                        candidates and perform unmixing
                                      0,9
                                      0,8        0,9
                                                 0,6
                                      M 0,4
                                        M
                                      0,1
                                        0,1
                                      0,2                            3. Split every pixel into n sub-pixels, and
                                                                        assign them to a class
                 0,9                  0,9
                 M
                 0,1                  M
                                      0,7
                                      0,1
                                      0,3                            4. Perform spatial regularization in order
                 M
                 0,9
                 0,1
                                                                        to correctly locate sub-pixels



          A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images
  at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011


         Jocelyn Chanussot                                                Gipsa-Lab                                        26 / 21
A new approach to classification                                      Experiments                                        Conclusions

 The proposed approach



                                                                   Proposed method
                          0,5 0,3
                            0,2   M                                We propose a technique in four steps:
    0,7
                                      M
                                      0,6                            1. Preliminary classification with
     M
    0,3                               0,4                               probabilistic classifier (SVM)

                                                                     2. Choose suitable endmember
                                                                        candidates and perform unmixing
                                      0,9
                                      0,8        0,9
                                                 0,6
                                      M 0,4
                                        M
                                      0,1
                                        0,1
                                      0,2                            3. Split every pixel into n sub-pixels, and
                                                                        assign them to a class
                 0,9                  0,9
                 M
                 0,1                  M
                                      0,7
                                      0,1
                                      0,3                            4. Perform spatial regularization in order
                 M
                 0,9
                 0,1
                                                                        to correctly locate sub-pixels



          A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images
  at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011


         Jocelyn Chanussot                                                Gipsa-Lab                                        26 / 21
A new approach to classification                            Experiments              Conclusions

 Simulated Annealing




       Minimize a given Cost Function introducing
       random perturbations:


           • decreases of the CF are always accepted
           • increases of the CF accepted with a probability
               inversely proportional to the degradation
           • probability of ’bad solutions’ decreases as the
               search continues




         Simulated Annealing optimization avoids local minima leading to global optimal
         solution




         Jocelyn Chanussot                                      Gipsa-Lab              27 / 21
A new approach to classification                          Experiments                                   Conclusions

 Simulated Annealing



         Cost function to be minimized: total perimeter of the connected areas (e.g.,
         belonging to the same class)


                                     M
                                   0,5 0,3
                                     0,2                                           M
                                                                                 0,5 0,3
                                                                                   0,2
                 0,7
                  M
                 0,3                         M
                                             0,6
                                             0,4
                                                                  0,7
                                                                   M
                                                                  0,3                      M
                                                                                           0,6
                                                                                           0,4



                                             0,9
                                             0,8   0,9
                                                   0,6                                     0,9
                                                                                           0,8   0,9
                                                                                                 0,6
                                             M 0,4
                                               M
                                             0,1
                                             0,2
                                               0,1                                         M 0,4
                                                                                             M
                                                                                           0,1
                                                                                           0,2
                                                                                             0,1
                             0,9             0,9                           0,9             0,9
                             M
                             0,1             M
                                             0,7
                                             0,1
                                             0,3                           M
                                                                           0,1             M
                                                                                           0,7
                                                                                           0,1
                                                                                           0,3

                             M
                             0,9
                             0,1                                           M
                                                                           0,9
                                                                           0,1

               Cost function not optimized                             Cost function optimized




         Jocelyn Chanussot                                    Gipsa-Lab                                   28 / 21
A new approach to classification                                  Experiments                                  Conclusions

 Simulated Annealing


                                             M
                                           0,5 0,3
                                             0,2                                          M
                                                                                        0,5 0,3
                                                                                          0,2
                             0,7
                             M
                             0,3                     M
                                                     0,6
                                                     0,4
                                                                          0,7
                                                                          M
                                                                          0,3                     M
                                                                                                  0,6
                                                                                                  0,4



                                                     0,9
                                                     0,8   0,9
                                                           0,6                                    0,9
                                                                                                  0,8   0,9
                                                                                                        0,6
                                                     M 0,4
                                                       M
                                                     0,1
                                                     0,2
                                                       0,1                                        M 0,4
                                                                                                    M
                                                                                                  0,1
                                                                                                  0,2
                                                                                                    0,1
                                     0,9             0,9                          0,9             0,9
                                     M
                                     0,1             M
                                                     0,7
                                                     0,1
                                                     0,3                          M
                                                                                  0,1             M
                                                                                                  0,7
                                                                                                  0,1
                                                                                                  0,3

                                     M
                                     0,9
                                     0,1                                          M
                                                                                  0,9
                                                                                  0,1


                                  Initial condition                                Iteration 1
                                             M
                                           0,5 0,3
                                             0,2                                          M
                                                                                        0,5 0,3
                                                                                          0,2
                             0,7
                             M
                             0,3                     M
                                                     0,6
                                                     0,4
                                                                          0,7
                                                                          M
                                                                          0,3                     M
                                                                                                  0,6
                                                                                                  0,4



                                                     0,9
                                                     0,8   0,9
                                                           0,6                                    0,9
                                                                                                  0,8   0,9
                                                                                                        0,6
                                                     M 0,4
                                                       M
                                                     0,1
                                                     0,2
                                                       0,1                                        M 0,4
                                                                                                    M
                                                                                                  0,1
                                                                                                  0,2
                                                                                                    0,1
                                     0,9             0,9                          0,9             0,9
                                     M
                                     0,1             M
                                                     0,7
                                                     0,1
                                                     0,3                          M
                                                                                  0,1             M
                                                                                                  0,7
                                                                                                  0,1
                                                                                                  0,3

                                     M
                                     0,9
                                     0,1                                          M
                                                                                  0,9
                                                                                  0,1


                                     Iteration n                                  Final result


         Jocelyn Chanussot                                            Gipsa-Lab                                  28 / 21
A new approach to classification                          Experiments                                  Conclusions

 Simulated Annealing



         Cost function to be minimized: total perimeter of the connected areas (e.g.,
         belonging to the same class)


                                     M
                                   0,5 0,3
                                     0,2                                          M
                                                                                0,5 0,3
                                                                                  0,2
                 0,7
                  M
                 0,3                         M
                                             0,6
                                             0,4
                                                                  0,7
                                                                   M
                                                                  0,3                     M
                                                                                          0,6
                                                                                          0,4



                                             0,9
                                             0,8   0,9
                                                   0,6                                    0,9
                                                                                          0,8   0,9
                                                                                                0,6
                                             M 0,4
                                               M
                                             0,1
                                               0,1
                                             0,2                                          M 0,4
                                                                                            M
                                                                                          0,1
                                                                                            0,1
                                                                                          0,2
                             0,9             0,9                          0,9             0,9
                             M
                             0,1             M
                                             0,7
                                             0,1
                                             0,3                          M
                                                                          0,1             M
                                                                                          0,7
                                                                                          0,1
                                                                                          0,3

                             M
                             0,9
                             0,1                                          M
                                                                          0,9
                                                                          0,1

               Cost function not optimized                             Minimum cost function




         Jocelyn Chanussot                                    Gipsa-Lab                                  28 / 21
A new approach to classification                                              Experiments                                           Conclusions

 Experiment on real data

         AVIRIS Indian Pine data set
             • (145×145 pixels, 220 bands), 16 classes of interest
             • Spatial resolution of the original data degraded of a factor 2
             • 10% of the labelled samples used as training set

         AVIRIS Hekla data set
             • (180×180 pixels, 157 bands), 9 classes of interest
             • Spatial resolution of the original data degraded of a factor 2
             • 15% of the labelled samples used as training set

         Comparison with SVM 1vs1, RBF kernel

                              20                                            20


                                                                            40
                              40
                                                                            60
                              60
                                                                            80

                              80                                           100


                                                                           120
                             100

                                                                           140
                             120
                                                                           160

                             140
                                                                           180
                                   20     40   60   80   100   120   140         20   40   60   80   100   120   140   160   180



                                        Indian Pine GT                                     Hekla GT
         Jocelyn Chanussot                                                        Gipsa-Lab                                           29 / 21
A new approach to classification   Experiments      Conclusions

 Evaluation of the results




         Jocelyn Chanussot             Gipsa-Lab      30 / 21
A new approach to classification   Experiments      Conclusions

 Evaluation of the results




         Jocelyn Chanussot             Gipsa-Lab      30 / 21
A new approach to classification   Experiments      Conclusions

 Evaluation of the results




         Jocelyn Chanussot             Gipsa-Lab      30 / 21
A new approach to classification                                    Experiments                                         Conclusions

 AVIRIS Indian Pine


                  20                                                     10



                  40                                                     20



                  60                                                     30


                  80                                                     40


                 100                                                     50


                 120                                                     60


                 140                                                     70
                         20       40   60   80   100   120   140                 10   20   30   40   50    60    70


                              Ground truth                                SVM map (OA = 72.31%)

                  20                                                      20


                  40                                                      40


                  60                                                      60


                  80                                                      80


                 100                                                     100


                 120                                                     120


                 140                                                     140
                         20       40   60   80   100   120   140                 20   40   60   80   100   120   140


          Proposed, before SA (OA = 89.82%)                        Proposed, final map (OA = 91.10%)

         Jocelyn Chanussot                                              Gipsa-Lab                                         31 / 21
A new approach to classification                                            Experiments                                                     Conclusions

 AVIRIS Hekla

                 10                                                              10


                 20                                                              20


                 30                                                              30


                 40                                                              40


                 50                                                              50


                 60                                                              60


                 70                                                              70


                 80                                                              80


                 90                                                              90
                       10    20    30   40   50    60    70    80    90                  10   20   30   40   50    60    70    80    90


                                  Low res. GT                                     SVM map (OA = 69.19%)

                  20                                                              20


                  40                                                              40


                  60                                                              60


                  80                                                              80


                 100                                                             100


                 120                                                             120


                 140                                                             140


                 160                                                             160


                 180                                                             180
                        20   40    60   80   100   120   140   160   180                 20   40   60   80   100   120   140   160   180


          Proposed, before SA (OA = 78.90%)                                Proposed, final map (OA = 81.71%)

         Jocelyn Chanussot                                                      Gipsa-Lab                                                     32 / 21
A new approach to classification                                                                                    Experiments                                                                                       Conclusions

 A robust method
                                                         AVIRIS Indian Pine (Complete)                                                                   AVIRIS Indian Pine (full data set)



                                            90                                                                                     90




                                            85                                                                                     85                                                         Proposed method
                     Overall Accuracy (%)




                                                                                                            Overall Accuracy (%)
                                                                                          Traditional SVM
                                                                                                                                                                                              Traditional SVM
                                                                                          Proposed Method


                                            80                                                                                     80




                                            75                                                                                     75




                                            70                                                                                     70

                                                 0.6   0.65           0.7            0.75             0.8                               5                  10                     15                            20
                                                              Treshold Pure Pixels                                                                      Number of ’candidates endmember’




                                                                 AVIRIS Hekla                                                                                     AVIRIS Hekla
                                            82                                                                                     82


                                            80                                                                                     80


                                            78                                                                                     78
                     Overall Accuracy (%)




                                                                                                            Overall Accuracy (%)
                                                                                         Proposed Method
                                            76                                                                                     76
                                                                                         Traditional SVM


                                            74                                                                                     74


                                            72                                                                                     72


                                            70                                                                                     70


                                            68                                                                                     68
                                                 0.6   0.65           0.7            0.75             0.8                               5                  10                     15                            20
                                                              Treshold Pure Pixels                                                                      Number of ’candidates endmember’




         Jocelyn Chanussot                                                                                                                  Gipsa-Lab                                                                   33 / 21
A new approach to classification                        Experiments                        Conclusions

 Conclusions and Perspectives




         New method to improve spatial resolution of thematic maps:
             • Spectral Unmixing considered to handle mixed pixels and abundances determination
             • Simulated Annealing proposed for spatial regularization




         Better definition of spatial structures with respect to full pixel classifiers when the
         image contains mixed pixels



         Large quantitative improvement




         Jocelyn Chanussot                                  Gipsa-Lab                        34 / 21

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chanussot.pdf

  • 1. Unsupervised classification and spectral unmixing for sub-pixel labelling A.Villa , ,† , J.Chanussot , J.A. Benediktsson , C.Jutten GIPSA-lab, Signal & Image Dept., Grenoble Institute of Technology, France. Faculty of Electrical and Computer Engineering, University of Iceland, Iceland. † Aresys, Politecnico di Milano, Italy. IEEE IGARSS 2011 Vancouver, Canada - 2011
  • 2. A new approach to classification Experiments Conclusions Hyperspectral Images Widely used in remote sensing: λ √ Wide spectral range and large number of wavelengths - Trees - Grass √ Very high spectral resolution VIS NIR 0.4 μm 2.4 μm × Tradeoff between spectral and spatial resolution Jocelyn Chanussot Gipsa-Lab 2 / 21
  • 3. A new approach to classification Experiments Conclusions Challenges Low spatial resolution → appearance of mixed pixels • Common in hyperspectral images Pure pixel: • Traditional classifiers inadequate, 100% grass partially addressed by mixed pixel techniques Mixed pixel: • Critical for land cover mapping 70% metal sheet 30% grass Joint use (full + mixed techniques) desirable, but little investigated [Wang and Jia, 2010]. Jocelyn Chanussot Gipsa-Lab 3 / 21
  • 4. A new approach to classification Experiments Conclusions Challenges Low spatial resolution → appearance of mixed pixels • Common in hyperspectral images Pure pixel: • Traditional classifiers inadequate, 100% grass partially addressed by mixed pixel techniques Mixed pixel: • Critical for land cover mapping 70% metal sheet 30% grass Incorporation of spectral unmixing in the classification process: • Does it provide accuracy improvement? • Is it possible to improve the classification map spatial resolution? Jocelyn Chanussot Gipsa-Lab 3 / 21
  • 5. A new approach to classification Experiments Conclusions 1 A new approach to classification 2 Experiments 3 Conclusions Jocelyn Chanussot Gipsa-Lab 4 / 21
  • 6. A new approach to classification Experiments Conclusions Context Traditional techniques neglect sub-pixel and spatial information Additional information provided by unmixing not fully exploited 0.6 0.9 Pure pixel: 100% grass 1 0.9 0.8 0.6 1 Mixed pixel: 1 1 0.8 70% metal sheet 30% grass 0.9 0.6 1 Original image Classification Unmixing Finer resolution? How to jointly use full and mixed pixel techniques? Jocelyn Chanussot Gipsa-Lab 5 / 21
  • 7. A new approach to classification Experiments Conclusions Proposed Approach Low resolution hyperpspectral data Unmixing Classes Abundances identification maps Classification "Upsampled" classification map Spatial regularization Final map Jocelyn Chanussot Gipsa-Lab 6 / 21
  • 8. A new approach to classification Experiments Conclusions Proposed Approach 1. Abundances fractions are computed from a HSI Step 1: Low resolution hyperpspectral data Pure pixel: 100% grass Step 1 Step 2 Mixed pixel: Classes Abundances 70% metal sheet identification maps 30% grass Step 2: "Upsampled" 0.6 classification map 0.9 1 0.9 0.8 Spatial regularization 0.6 1 1 1 0.8 Final map 0.9 0.6 1 Jocelyn Chanussot Gipsa-Lab 7 / 21
  • 9. A new approach to classification Experiments Conclusions The proposed approach M = Mixed pixel M Proposed method M M The abundances computation is in two steps, to take the spatial information into account: 1. Pixels with an abundance over a certain threshold are considered ’pure’ M M 2. Abundances of ’mixed’ pixels are M M computed by selecting as endmembers pixels spatially close M Jocelyn Chanussot Gipsa-Lab 8 / 21
  • 10. A new approach to classification Experiments Conclusions The proposed approach M = Mixed pixel Proposed method The abundances computation is in two steps, to take the spatial information into account: 1. Pixels with an abundance over a certain threshold are considered ’pure’ 2. Abundances of ’mixed’ pixels are computed by selecting as endmembers pixels spatially close Jocelyn Chanussot Gipsa-Lab 8 / 21
  • 11. A new approach to classification Experiments Conclusions Proposed Approach 2. Creation of a finer classification map Step 2: 0.6 Low resolution hyperpspectral data 0.9 1 0.9 0.8 0.6 1 Step 2 1 1 0.8 Classes Abundances identification maps 0.9 0.6 1 Step 3 "Upsampled" Step 3: classification map Spatial regularization Final map Jocelyn Chanussot Gipsa-Lab 9 / 21
  • 12. A new approach to classification Experiments Conclusions Proposed Approach 3. Final spatial regularization Step 3: Low resolution hyperpspectral data Classes Abundances identification maps Step 3 "Upsampled" Step 4: classification map Step 4 Spatial regularization Final map Jocelyn Chanussot Gipsa-Lab 10 / 21
  • 13. A new approach to classification Experiments Conclusions Spatial regularization Criterion: minimization of the total perimeter of the connected areas (e.g., belonging to the same class) M 0,5 0,3 0,2 M 0,5 0,3 0,2 0,7 M 0,3 M 0,6 0,4 0,7 M 0,3 M 0,6 0,4 0,9 0,8 0,9 0,6 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,1 0,2 M 0,4 M 0,1 0,1 0,2 0,9 0,9 0,9 0,9 M 0,1 M 0,7 0,1 0,3 M 0,1 M 0,7 0,1 0,3 M 0,9 0,1 M 0,9 0,1 Criterion not satisfied Criterion satisfied Jocelyn Chanussot Gipsa-Lab 11 / 21
  • 14. A new approach to classification Experiments Conclusions Spectral unmixing based approach [Villa Novelties introduced: et al., 2010] 1. Retrieve classes with unsupervised 1. VCA for class retrieval clustering (→ more robust to outliers) 2. FCLS for abundance determination 2. Include spatial information (→ use more accurate 3. Simulated Annealing for spatial endmembers) regularization Jocelyn Chanussot Gipsa-Lab 12 / 21
  • 15. A new approach to classification Experiments Conclusions VCA vs. K-MEANS 6000 6000 5000 5000 4000 4000 3000 3000 2000 2000 1000 1000 0 0 4000 4000 2000 2000 6000 4000 5000 6000 0 2000 3000 4000 5000 0 1000 2000 3000 0 1000 0 VCA K-MEANS Jocelyn Chanussot Gipsa-Lab 13 / 21
  • 16. A new approach to classification Experiments Conclusions 1 A new approach to classification 2 Experiments 3 Conclusions Jocelyn Chanussot Gipsa-Lab 14 / 21
  • 17. A new approach to classification Experiments Conclusions How to verify the results? Decrease original resolution Final map Proposed (sub-pixel precision) approach Jocelyn Chanussot Gipsa-Lab 15 / 21
  • 18. A new approach to classification Experiments Conclusions Experiments on real data ROSIS University data set AISA data set • Classification of a metal sheet roof • 400×500 pixels area, six classes of (120×90 pixels) interest • 1.3 m spatial resolution, 103 • 6 m spatial resolution, 252 spectral spectral bands. bands • Spatial resolution of the original • Spatial resolution of the original data data degraded of a factor 3 degraded of a factor 5 Jocelyn Chanussot Gipsa-Lab 16 / 21
  • 19. A new approach to classification Experiments Conclusions Real data sets ROSIS data set: Original Image K-means (93.75%) VCA+SU (96.95%) KM+SU (95.89%) Jocelyn Chanussot Gipsa-Lab 17 / 21
  • 20. A new approach to classification Experiments Conclusions Real data set AISA data set: K-means (51.61%) VCA+SU (59.69%) KM+SU (75.72%) Jocelyn Chanussot Gipsa-Lab 18 / 21
  • 21. A new approach to classification Experiments Conclusions 1 A new approach to classification 2 Experiments 3 Conclusions Jocelyn Chanussot Gipsa-Lab 19 / 21
  • 22. A new approach to classification Experiments Conclusions Conclusions and Perspectives New method to improve spatial resolution of thematic maps: • Unsupervised clustering to define classes • Integration of spatial information to locally model abundances • Simulated Annealing proposed for spatial regularization Clustering less sensitive to extreme pixels, VCA better in highly mixed scenarios Next step: Incorporate spectral variability of the classes Jocelyn Chanussot Gipsa-Lab 20 / 21
  • 23. A new approach to classification Experiments Conclusions Unsupervised classification and spectral unmixing for sub-pixel labelling A.Villa , ,† , J.Chanussot , J.A. Benediktsson , C.Jutten GIPSA-lab, Signal & Image Dept., Grenoble Institute of Technology, France. Faculty of Electrical and Computer Engineering, University of Iceland, Iceland. † Aresys, Politecnico di Milano, Italy. IEEE IGARSS 2011 Vancouver, Canada - 2011 Jocelyn Chanussot Gipsa-Lab 21 / 21
  • 24. A new approach to classification Experiments Conclusions Challenges Hyperspectral images issues: 1 Curse of dimensionality 2 Exploitation of contextual information 3 Presence of mixed pixels • Common in hyperspectral images Pure pixel: 100% grass • Traditional classifiers inadequate Mixed pixel: • Usually not considered for 70% metal sheet classification! 30% grass Jocelyn Chanussot Gipsa-Lab 22 / 21
  • 25. A new approach to classification Experiments Conclusions Context Traditional approaches to image analysis are full pixel and mixed pixel techniques • Full pixel techniques are traditional classification algorithms • Mixed pixel techniques are spectral unmixing, soft classification, . . . Joint use is desirable, but little investigated [Wang and Jia, 2010]. Incorporation of spectral unmixing in the classification process: • Does it provide accuracy improvement? • Is it possible to improve the classification map spatial resolution? Jocelyn Chanussot Gipsa-Lab 23 / 21
  • 26. A new approach to classification Experiments Conclusions Linear Spectral Unmixing Abundances estimation through spectral unmixing: • Goal: find extreme pixels (endmembers) that can be used to "unmix" other pixels. • Each "mixed" pixel is a combination of endmember fractional abundances. Jocelyn Chanussot Gipsa-Lab 24 / 21
  • 27. A new approach to classification Experiments Conclusions Linear Spectral Unmixing Abundances estimation through spectral unmixing: • Goal: find extreme pixels (endmembers) that can be used to "unmix" other pixels. • Each "mixed" pixel is a combination of endmember fractional abundances. Jocelyn Chanussot Gipsa-Lab 24 / 21
  • 28. A new approach to classification Experiments Conclusions Context Traditional techniques neglect information Additional information provided by unmixing not fully exploited 0.6 0.9 Pure pixel: 100% grass 1 0.9 0.8 0.6 1 Mixed pixel: 1 1 0.8 70% metal sheet 30% grass 0.9 0.6 1 Original image Classification Unmixing Finer resolution? How to jointly use full and mixed pixel techniques? Jocelyn Chanussot Gipsa-Lab 25 / 21
  • 29. A new approach to classification Experiments Conclusions The proposed approach M = Mixed pixel Proposed method M We propose a technique in four steps: M M 1. Preliminary classification with probabilistic classifier (SVM) 2. Choose suitable endmember candidates and perform unmixing M M 3. Split every pixel into n sub-pixels, and assign them to a class M M 4. Perform spatial regularization in order M to correctly locate sub-pixels A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011 Jocelyn Chanussot Gipsa-Lab 26 / 21
  • 30. A new approach to classification Experiments Conclusions The proposed approach M = Mixed pixel Proposed method We propose a technique in four steps: 1. Preliminary classification with probabilistic classifier (SVM) 2. Choose suitable endmember candidates and perform unmixing 3. Split every pixel into n sub-pixels, and assign them to a class 4. Perform spatial regularization in order to correctly locate sub-pixels A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011 Jocelyn Chanussot Gipsa-Lab 26 / 21
  • 31. A new approach to classification Experiments Conclusions The proposed approach Proposed method 0,5 0,3 0,2 M We propose a technique in four steps: 0,7 M 0,6 1. Preliminary classification with M 0,3 0,4 probabilistic classifier (SVM) 2. Choose suitable endmember candidates and perform unmixing 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,1 0,2 3. Split every pixel into n sub-pixels, and assign them to a class 0,9 0,9 M 0,1 M 0,7 0,1 0,3 4. Perform spatial regularization in order M 0,9 0,1 to correctly locate sub-pixels A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011 Jocelyn Chanussot Gipsa-Lab 26 / 21
  • 32. A new approach to classification Experiments Conclusions The proposed approach Proposed method 0,5 0,3 0,2 M We propose a technique in four steps: 0,7 M 0,6 1. Preliminary classification with M 0,3 0,4 probabilistic classifier (SVM) 2. Choose suitable endmember candidates and perform unmixing 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,1 0,2 3. Split every pixel into n sub-pixels, and assign them to a class 0,9 0,9 M 0,1 M 0,7 0,1 0,3 4. Perform spatial regularization in order M 0,9 0,1 to correctly locate sub-pixels A. Villa, J. Chanussot, J.A. Benediktsson, C. Jutten, "Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution", IEEE J. Sel. Topics Sign. Proc., vol. 5, n. 3, 2011 Jocelyn Chanussot Gipsa-Lab 26 / 21
  • 33. A new approach to classification Experiments Conclusions Simulated Annealing Minimize a given Cost Function introducing random perturbations: • decreases of the CF are always accepted • increases of the CF accepted with a probability inversely proportional to the degradation • probability of ’bad solutions’ decreases as the search continues Simulated Annealing optimization avoids local minima leading to global optimal solution Jocelyn Chanussot Gipsa-Lab 27 / 21
  • 34. A new approach to classification Experiments Conclusions Simulated Annealing Cost function to be minimized: total perimeter of the connected areas (e.g., belonging to the same class) M 0,5 0,3 0,2 M 0,5 0,3 0,2 0,7 M 0,3 M 0,6 0,4 0,7 M 0,3 M 0,6 0,4 0,9 0,8 0,9 0,6 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,2 0,1 M 0,4 M 0,1 0,2 0,1 0,9 0,9 0,9 0,9 M 0,1 M 0,7 0,1 0,3 M 0,1 M 0,7 0,1 0,3 M 0,9 0,1 M 0,9 0,1 Cost function not optimized Cost function optimized Jocelyn Chanussot Gipsa-Lab 28 / 21
  • 35. A new approach to classification Experiments Conclusions Simulated Annealing M 0,5 0,3 0,2 M 0,5 0,3 0,2 0,7 M 0,3 M 0,6 0,4 0,7 M 0,3 M 0,6 0,4 0,9 0,8 0,9 0,6 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,2 0,1 M 0,4 M 0,1 0,2 0,1 0,9 0,9 0,9 0,9 M 0,1 M 0,7 0,1 0,3 M 0,1 M 0,7 0,1 0,3 M 0,9 0,1 M 0,9 0,1 Initial condition Iteration 1 M 0,5 0,3 0,2 M 0,5 0,3 0,2 0,7 M 0,3 M 0,6 0,4 0,7 M 0,3 M 0,6 0,4 0,9 0,8 0,9 0,6 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,2 0,1 M 0,4 M 0,1 0,2 0,1 0,9 0,9 0,9 0,9 M 0,1 M 0,7 0,1 0,3 M 0,1 M 0,7 0,1 0,3 M 0,9 0,1 M 0,9 0,1 Iteration n Final result Jocelyn Chanussot Gipsa-Lab 28 / 21
  • 36. A new approach to classification Experiments Conclusions Simulated Annealing Cost function to be minimized: total perimeter of the connected areas (e.g., belonging to the same class) M 0,5 0,3 0,2 M 0,5 0,3 0,2 0,7 M 0,3 M 0,6 0,4 0,7 M 0,3 M 0,6 0,4 0,9 0,8 0,9 0,6 0,9 0,8 0,9 0,6 M 0,4 M 0,1 0,1 0,2 M 0,4 M 0,1 0,1 0,2 0,9 0,9 0,9 0,9 M 0,1 M 0,7 0,1 0,3 M 0,1 M 0,7 0,1 0,3 M 0,9 0,1 M 0,9 0,1 Cost function not optimized Minimum cost function Jocelyn Chanussot Gipsa-Lab 28 / 21
  • 37. A new approach to classification Experiments Conclusions Experiment on real data AVIRIS Indian Pine data set • (145×145 pixels, 220 bands), 16 classes of interest • Spatial resolution of the original data degraded of a factor 2 • 10% of the labelled samples used as training set AVIRIS Hekla data set • (180×180 pixels, 157 bands), 9 classes of interest • Spatial resolution of the original data degraded of a factor 2 • 15% of the labelled samples used as training set Comparison with SVM 1vs1, RBF kernel 20 20 40 40 60 60 80 80 100 120 100 140 120 160 140 180 20 40 60 80 100 120 140 20 40 60 80 100 120 140 160 180 Indian Pine GT Hekla GT Jocelyn Chanussot Gipsa-Lab 29 / 21
  • 38. A new approach to classification Experiments Conclusions Evaluation of the results Jocelyn Chanussot Gipsa-Lab 30 / 21
  • 39. A new approach to classification Experiments Conclusions Evaluation of the results Jocelyn Chanussot Gipsa-Lab 30 / 21
  • 40. A new approach to classification Experiments Conclusions Evaluation of the results Jocelyn Chanussot Gipsa-Lab 30 / 21
  • 41. A new approach to classification Experiments Conclusions AVIRIS Indian Pine 20 10 40 20 60 30 80 40 100 50 120 60 140 70 20 40 60 80 100 120 140 10 20 30 40 50 60 70 Ground truth SVM map (OA = 72.31%) 20 20 40 40 60 60 80 80 100 100 120 120 140 140 20 40 60 80 100 120 140 20 40 60 80 100 120 140 Proposed, before SA (OA = 89.82%) Proposed, final map (OA = 91.10%) Jocelyn Chanussot Gipsa-Lab 31 / 21
  • 42. A new approach to classification Experiments Conclusions AVIRIS Hekla 10 10 20 20 30 30 40 40 50 50 60 60 70 70 80 80 90 90 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 Low res. GT SVM map (OA = 69.19%) 20 20 40 40 60 60 80 80 100 100 120 120 140 140 160 160 180 180 20 40 60 80 100 120 140 160 180 20 40 60 80 100 120 140 160 180 Proposed, before SA (OA = 78.90%) Proposed, final map (OA = 81.71%) Jocelyn Chanussot Gipsa-Lab 32 / 21
  • 43. A new approach to classification Experiments Conclusions A robust method AVIRIS Indian Pine (Complete) AVIRIS Indian Pine (full data set) 90 90 85 85 Proposed method Overall Accuracy (%) Overall Accuracy (%) Traditional SVM Traditional SVM Proposed Method 80 80 75 75 70 70 0.6 0.65 0.7 0.75 0.8 5 10 15 20 Treshold Pure Pixels Number of ’candidates endmember’ AVIRIS Hekla AVIRIS Hekla 82 82 80 80 78 78 Overall Accuracy (%) Overall Accuracy (%) Proposed Method 76 76 Traditional SVM 74 74 72 72 70 70 68 68 0.6 0.65 0.7 0.75 0.8 5 10 15 20 Treshold Pure Pixels Number of ’candidates endmember’ Jocelyn Chanussot Gipsa-Lab 33 / 21
  • 44. A new approach to classification Experiments Conclusions Conclusions and Perspectives New method to improve spatial resolution of thematic maps: • Spectral Unmixing considered to handle mixed pixels and abundances determination • Simulated Annealing proposed for spatial regularization Better definition of spatial structures with respect to full pixel classifiers when the image contains mixed pixels Large quantitative improvement Jocelyn Chanussot Gipsa-Lab 34 / 21