Mapping Ash Tree Colonization in an Agricultural Moutain Landscape_ Investigation the Potential of Hyperspectral Imagery.pdf

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  • 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. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  • 3. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  • 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. 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. 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. 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. 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. Area covered by the missionVillage of Villelongue, France (00◦ 03’W and 42◦ 57’N).Medium altitudinal range (450-1800m) I         
  • 10. Field data 1/2Collecting tree species:
  • 11. Field data 2/2Biophysical parameters: Density Age Diameter Dominant height Topography Chlorophyll and nitrogen content (GPS position)
  • 12. Some ash examples
  • 13. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  • 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. 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. 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. 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. 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. 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. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  • 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. 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. MADONNA ProjectImage classificationExperimental resultsConclusions and perspectives
  • 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. 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