• Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,535
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
72
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Change detection in Hyperspectral data Dr. Prashanth Marpu (marpu@hi.is) Prof. Jon Atli Benediktsson University of Iceland, Reykjavik, Iceland Prof. Paolo Gamba University of Pavia, Pavia, Italy
  • 2. Overview
    • Background
    • IR-MAD
    • Multi-spectral case
    • Hyperspectral case
    • Ongoing work
    • Conclusions
  • 3. Background
    • With the increasing availability of remote sensing data, there is a huge interest in automatic change detection methods.
    • Methods such as CVA, IR-MAD are employed for multispectral change detection.
    • These methods can not be directly applied to the hyperspectral data due to the dimensionality of the data.
    • In this study, we investigate the effective adaptation of IR-MAD method for the hyperspectral case.
  • 4. Our approach Ongoing Work FR Time 1 Time 2 Initial change mask Change detection using IR-MAD method Post-processing Change map
  • 5. 2002 2003 D = a T F - b T G
    • Determination of a and b , so that the positive correlation between U = a T F and V = b T G is minimized.
    • Canonical correlation analysis (Hotelling, 1936).
    • Fully automatic scheme gives regularized iterated MAD variates, invariant to linear/affine transformations, orthogonal.
    • (Nielsen et al, 1998; Nielsen, 2007)
    U = a T F V = b T G MADs 2 (R), 3 (G), 4 (B) Multivariate Alteration Detection
  • 6. IR-MAD
    • Probalities of no-changes employed as weights for the observations in the next iteration.
    • Iterations until a convergence criterion is reached.
  • 7. Issues with change detection
    • Images can have completely different acquisition conditions which affect the quality of the images in many ways.
    • The atmospheric influence is very random.
    • = > Can be solved by IR-MAD under the linear assumption
    • Change detection methods often fail when there are huge changes between the two times.
    • => ...not solved by IR-MAD.
  • 8. Initial Change Mask (ICM)
    • The standard change detection methods fail when there are more change pixels compared to no-change pixels.
    • Simple adaptation where
      • strong changes are first eliminated by creating an initial change mask (ICM).
      • standard methods are then used with the remaining pixels [P. Marpu et al. , 2011] .
    • P. Marpu et al. , ``Improving change detection results of IR-MAD by eliminating strong changes‘‘, IEEE Geosci. And Remote Sensing , 2011
  • 9. ICM
    • Hyperspectral images
    • PCA is performed on the difference image and the first three PCs are selected.
    • The PCs are divided into three classes either using EM algorithm or any unsupervised classification algorithm such as k-means.
    • Pixels belonging to the second cluster : no-change pixels in the ICM.
    • These pixels are used for further analysis.
    • P. Gong, “Change detection using principal component analysis and fuzzy set theory.” Can. J. Remote Sens ., vol. 19, pp. 22–29, 1993.
  • 10. Multispectral change detection using IR-MAD and initial change mask.
  • 11. Experiments with ICM Landsat ETM+ images over Juelich, Germany taken in May, June and August, 2001. Major changes due to agricultural regions. Only the human settlement areas and mining area remain unchanged.
  • 12. Experiments May- June May- August
  • 13. Experiments Without Mask With Mask May -June
  • 14. Experiments
  • 15. Experiments Without Mask With Mask Automatic radiometric normalization
  • 16. Experiments May- August Without Mask With Mask
  • 17. Experiments Without Mask With Mask Automatic radiometric normalization
  • 18. Feature reduction for IR-MAD
    • MAD is invariant to linear/affine transformations.
    • Let P , Q be the PCA transformation images of the image pair F , G .
    • The MAD variates, M i , can be expressed in terms of P , Q as
    • M i =U i -V i = c i T P - d i T P ,
    • where c i , d i eigenvectors of the generalized eigenvalue problem.
    • Main assumption: components with low eigenvalues are noise components
    • => Do not contribute to the correlation of the no-change pixels
    • => Do not contribute to the top MAD variates
    • PCs with low eigenvalues are then ignored , hence facilitating hyperspectral change detection.
  • 19. Effect of feature reduction in the multispectral case Comparison of the slopes for radiometric correction using manually selected no-change pixels and automatically selected no-change pixels using different inputs.
  • 20.
    • Hyperspectral change detection
  • 21. AISA Eagle data from Hungary (2007, 2008) 252 bands ICM using difference vector Chi-square image resulting from 8 PCs
  • 22. CHRIS-PROBA images from Tor Vergata, Rome February , 2006 18 bands October, 2006 Chi- Square image from 6 PCs
  • 23. Ongoing work
    • To generate a final Change/ No-change classification map.
    • One approach is to use the EM algorithm to estimate the no-change distribution, but the number of Gaussian components can not be determined in advance.
    • We employ a method based on Markov Random Fields to determine the Change / No-change labels
  • 24. Ongoing work
    • Hidden labels assigned to all the image pixels (partition the image in 3 classes)
    • Markov random fields as a prior label distribution in a Bayesian approach
    • Gibbs sampler as a simulation method
    Histograms of (left) the original image, EM estimation (center), ongoing approach(right)
  • 25. Chi-sqr image Change/No-change labels Change Classification
  • 26. Conclusion
    • IR-MAD method is succesfully applied to the hyperspectral images by feature reduction with PCA.
    • Initial change mask to be used in the case of presence of more change pixels compared to no-change pixels.
    • Ongoing work to fuse spatial information using MRFs to deduce a final change map.
  • 27.
    • Thank You for your attention