mapping ash tree colonization in an agricultural moutain landscape_ investigation the potential of...
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Mapping Ash Tree Colonization in anAgricultural Moutain Landscape: Investigation
the Potential of Hyperspectral Imagery
D. Sheeren1,2, M. Fauvel1,2, S. Ladet2, A. Jacquin2, G. Bertoni1,2 andA. Gibon2
1 INP Toulouse - University of Toulouse2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan
2011 IEEE International Geoscience and Remote Sensing Symposium
24-29 July, Vancouver, Canada
MADONNA Project
Image classification
Experimental results
Conclusions and perspectives
MADONNA Project
Image classification
Experimental results
Conclusions and perspectives
Context of the work 1/3
Scientific context:
Landscape ecology:
I What is the impact of agricultural activities on the landscape?I What is the impact of landscape heterogeneity on the biodiversity?
Global change:
I How evolve landscape and biodiversity?I What are the factors of evolution?
Predict and anticipate the responses of ecosystems to landscape changes.
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:
X Ecological process understood
X Multi-agents model build
× Accurate ash-thematic map
Context of the work 3/3
Current method: Aerial images + visual inspection + field survey
I Small geographical areaI Time consumingI Cost !
Multispectral satellite images are not enough spatially and spectrallyaccurate 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 todifferentiate between ash tree and other species of tree
Madonna Project!
Context of the work 3/3
Current method: Aerial images + visual inspection + field survey
I Small geographical areaI Time consumingI Cost !
Multispectral satellite images are not enough spatially and spectrallyaccurate 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 todifferentiate between ash tree and other species of tree
Madonna Project!
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 . . . )
Area covered by the mission
Village of Villelongue, France (00◦03’W and 42◦57’N).Medium altitudinal range (450-1800m)
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Field data 1/2
Collecting tree species:
Field data 2/2
Biophysical parameters:
Density
Age
Diameter
Dominant height
Topography
Chlorophyll and nitrogen content
(GPS position)
Some ash examples
MADONNA Project
Image classification
Experimental results
Conclusions and perspectives
Very high spatial resolution hyperspectral images 1/2
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6
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HySpex sensor:
Spectral resolution : 1.5 nm, 400-1000 nm, 160 bands
Spatial resolution: 50 cm
Very high spatial resolution hyperspectral images 2/2
Pattern 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 oftime 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 neededfor an operational system
SVM + Kernel alignment
Very high spatial resolution hyperspectral images 2/2
Pattern 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 oftime 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 neededfor an operational system
SVM + Kernel alignment
Support Vector Machine
yi = 1
yi = −1
w
H (α, b) : {x|f (x) = 0}
Supervised method: S ={
(x1, y1), . . . , (xn, yn)}∈ Rd × {−1; 1}
Separating function: f (z) = sgn( n∑
i=1αik(z,xi) + b
)Solve QP problem:
maxα
g(α) =n∑
i=1αi −
12
n∑i,j=1
αiαjyiyjk(xi ,xj)
constraint to 0 ≤ αi ≤ C et∑n
i=1 αiyi = 0
Kernel functionKernel function k: similarity measure between two spectra xi et xj
Gaussian kernel : kg(xi ,xj) = exp(− ‖xi − xj‖2
2σ2
)σ2 is an hyperparameter that controls how two spectra are considered assimilar or not.
Ideally, σ2 must be tuned such as:
kg(xi ,xj) ≈ 1 If yi = yj
kg(xi ,xj) ≈ 0 Else
Ideal kernel matrix:
KI =
1 δy1y2 . . . δy1yn
δy2y1 1 . . . δy2yn
......
. . ....
δyny1 δyny2 . . . 1
Kernel alignment
Alignment A: compute the similitude (angle) between the ideal matrix andthe kernel matrix with parameter σ2
A(σ) = 〈K I ,K 〉F‖K I‖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 20 40 600.25
0.3
0.35
0.4
0.45
0.5
MADONNA Project
Image classification
Experimental results
Conclusions and perspectives
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?
ResultsQuantitative analysis: Ash tree separability
GMM SVM SVM-linOA 72% 94% 89%Kappa 0.65 0.92 0.89
Ash treeUser accuracy 84.0% 89.9% 83.1%Producer accuracy 53.6% 89.9% 88.8%
Qualitative analysis:
MADONNA Project
Image classification
Experimental results
Conclusions and perspectives
Conclusions and perspectives
Conclusions:
Accurate mapping of ash tree is possible with hyperspectral images
Framework: SVM + kernel alignment
Perspectives:
Spatial regularization
Can biophysical parameters be estimated?
Mapping Ash Tree Colonization in anAgricultural Moutain Landscape: Investigation
the Potential of Hyperspectral Imagery
D. Sheeren1,2, M. Fauvel1,2, S. Ladet2, A. Jacquin2, G. Bertoni1,2 andA. Gibon2
1 INP Toulouse - University of Toulouse2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan
2011 IEEE International Geoscience and Remote Sensing Symposium
24-29 July, Vancouver, Canada