Mapping Ash Tree Colonization in an Agricultural Moutain Landscape: Investigation    the Potential of Hyperspectral Imager...
MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
Context of the work 1/3Scientific context:    Landscape ecology:         What is the impact of agricultural activities on t...
Context of the work 2/3What are the causes and the consequences of ash three colonization in thePyrenees mountain?        ...
Context of the work 3/3 Current method: Aerial images + visual inspection + field survey     Small geographical area     Ti...
Context of the work 3/3 Current method: Aerial images + visual inspection + field survey     Small geographical area     Ti...
Madonna project: Objectives and data Objectives:   • Mapping of the ash tree distribution   • 2D et 3D information   • Est...
Area covered by the missionVillage of Villelongue, France (00◦ 03’W and 42◦ 57’N).Medium altitudinal range (450-1800m)    ...
Field data 1/2Collecting tree species:
Field data 2/2Biophysical parameters:                          Density                          Age                       ...
Some ash examples
MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
Very high spatial resolution hyperspectral images 1/2                  T                                                  ...
Very high spatial resolution hyperspectral images 2/2Pattern recognition approach:    Problem of the spectral dimensionali...
Very high spatial resolution hyperspectral images 2/2Pattern recognition approach:    Problem of the spectral dimensionali...
Support Vector Machine                                                              H (α, b) : {x|f (x) = 0}              ...
Kernel function Kernel function k: similarity measure between two spectra xi et xj                                        ...
Kernel alignment Alignment A: compute the similitude (angle) between the ideal matrix and the kernel matrix with parameter...
MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
Protocol Ground thruth: 12 tree species (Ash tree, Chestnut tree, Lime tree, Hazel tree . . . ). Classification of the tree...
Results Quantitative analysis: Ash tree separability                                  GMM        SVM   SVM-lin            ...
MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
Conclusions and perspectivesConclusions:    Accurate mapping of ash tree is possible with hyperspectral images    Framewor...
Mapping Ash Tree Colonization in an Agricultural Moutain Landscape: Investigation    the Potential of Hyperspectral Imager...
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Mapping Ash Tree Colonization in an Agricultural Moutain Landscape_ Investigation the Potential of Hyperspectral Imagery.pdf

  1. 1. Mapping Ash Tree Colonization in an Agricultural Moutain Landscape: Investigation the Potential of Hyperspectral Imagery D. Sheeren1,2 , M. Fauvel1,2 , S. Ladet2 , A. Jacquin2 , G. Bertoni1,2 and A. Gibon2 1 INP Toulouse - University of Toulouse 2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan2011 IEEE International Geoscience and Remote Sensing Symposium 24-29 July, Vancouver, Canada
  2. 2. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  3. 3. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  4. 4. Context of the work 1/3Scientific context: Landscape ecology: What is the impact of agricultural activities on the landscape? What is the impact of landscape heterogeneity on the biodiversity? Global change: How evolve landscape and biodiversity? What are the factors of evolution? Predict and anticipate the responses of ecosystems to landscape changes.
  5. 5. Context of the work 2/3What are the causes and the consequences of ash three colonization in thePyrenees mountain? Villelongue village, 65260-France 1950 2000Up to now: Ecological process understood Multi-agents model build × Accurate ash-thematic map
  6. 6. Context of the work 3/3 Current method: Aerial images + visual inspection + field survey Small geographical area Time consuming Cost ! Multispectral satellite images are not enough spatially and spectrally accurate for ash detection tree/not tree ok, but it is not possible to go to the species Good multitemporal data are very difficult to obtain Hypothesis: With hyperspectral images, it would be possible to differentiate between ash tree and other species of tree
  7. 7. Context of the work 3/3 Current method: Aerial images + visual inspection + field survey Small geographical area Time consuming Cost ! Multispectral satellite images are not enough spatially and spectrally accurate for ash detection tree/not tree ok, but it is not possible to go to the species Good multitemporal data are very difficult to obtain Hypothesis: With hyperspectral images, it would be possible to differentiate between ash tree and other species of tree Madonna Project!
  8. 8. Madonna project: Objectives and data Objectives: • Mapping of the ash tree distribution • 2D et 3D information • Estimation of structural and biophysical parameters (tree density and height, foliar chlorophyll . . . ) Data (summer 2010). • Very high spatial resolution hyperspectral images • LiDar data • Field data (ash trees and other dominant species, foliar analysis . . . )
  9. 9. Area covered by the missionVillage of Villelongue, France (00◦ 03’W and 42◦ 57’N).Medium altitudinal range (450-1800m) I
  10. 10. Field data 1/2Collecting tree species:
  11. 11. Field data 2/2Biophysical parameters: Density Age Diameter Dominant height Topography Chlorophyll and nitrogen content (GPS position)
  12. 12. Some ash examples
  13. 13. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  14. 14. Very high spatial resolution hyperspectral images 1/2 T         E  HySpex sensor: Spectral resolution : 1.5 nm, 400-1000 nm, 160 bands Spatial resolution: 50 cm
  15. 15. Very high spatial resolution hyperspectral images 2/2Pattern recognition approach: Problem of the spectral dimensionality: statistical methods fail Use of non-linear SVM (Gaussian kernel) But the optimization of the hyperparameter is too demanding in terms of time processing when using the conventional cross-validation strategy (about 3 To of data to process). A fast and accurate method for optimizing the hyperparameter is needed for an operational system SVM + Kernel alignment
  16. 16. Very high spatial resolution hyperspectral images 2/2Pattern recognition approach: Problem of the spectral dimensionality: statistical methods fail Use of non-linear SVM (Gaussian kernel) But the optimization of the hyperparameter is too demanding in terms of time processing when using the conventional cross-validation strategy (about 3 To of data to process). A fast and accurate method for optimizing the hyperparameter is needed for an operational system SVM + Kernel alignment
  17. 17. Support Vector Machine H (α, b) : {x|f (x) = 0} yi = 1 w yi = −1 Supervised method: S = (x1 , y1 ), . . . , (xn , yn ) ∈ Rd × {−1; 1} n Separating function: f (z) = sgn αi k(z, xi ) + b i=1 Solve QP problem: n n 1 max g(α) = αi − αi αj yi yj k(xi , xj ) α i=1 2 i,j=1 n constraint to 0 ≤ αi ≤ C et i=1 αi yi = 0
  18. 18. Kernel function Kernel function k: similarity measure between two spectra xi et xj 2 xi − xj Gaussian kernel : kg (xi , xj ) = exp − 2σ 2 σ 2 is an hyperparameter that controls how two spectra are considered as similar or not. Ideally, σ 2 must be tuned such as: kg (xi , xj ) ≈ 1 If yi = yj kg (xi , xj ) ≈ 0 Else Ideal kernel matrix:   1 δ y1 y2 . . . δy1 yn  δ y2 y1 1 . . . δy2 yn   I  K = .  . . .. .   . . . . . .   δyn y1 δyn y2 ... 1
  19. 19. Kernel alignment Alignment A: compute the similitude (angle) between the ideal matrix and the kernel matrix with parameter σ 2 KI, K F A(σ) = KI F K F σ is selected such A(σ) is maximal Contrary to cross-validation, there is no need to solve the QP problem 0.5 0.45 0.4 0.35 0.3 0.25 0 20 40 60
  20. 20. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  21. 21. Protocol Ground thruth: 12 tree species (Ash tree, Chestnut tree, Lime tree, Hazel tree . . . ). Classification of the tree species: Are ash trees identifiable? Classification of the image: Are the results spatially consistent?
  22. 22. Results Quantitative analysis: Ash tree separability GMM SVM SVM-lin OA 72% 94% 89% Kappa 0.65 0.92 0.89 Ash tree User accuracy 84.0% 89.9% 83.1% Producer accuracy 53.6% 89.9% 88.8% Qualitative analysis:
  23. 23. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  24. 24. Conclusions and perspectivesConclusions: Accurate mapping of ash tree is possible with hyperspectral images Framework: SVM + kernel alignmentPerspectives: Spatial regularization Can biophysical parameters be estimated?
  25. 25. Mapping Ash Tree Colonization in an Agricultural Moutain Landscape: Investigation the Potential of Hyperspectral Imagery D. Sheeren1,2 , M. Fauvel1,2 , S. Ladet2 , A. Jacquin2 , G. Bertoni1,2 and A. Gibon2 1 INP Toulouse - University of Toulouse 2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan2011 IEEE International Geoscience and Remote Sensing Symposium 24-29 July, Vancouver, Canada

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