Jorge Prendes

Jorge Prendes

(IRIT, University of Toulouse and SONDRA, CentraleSupelec, FR)

https://s3-seminar.github.io/seminars/jorge-prendes

Title — Analysis of remote sensing multi-sensor heterogeneous images

Abstract — Remote sensing images are images of the Earth acquired from planes or satellites. In recent years the technology enabling this kind of images has been evolving really fast. Many different sensors have been developed to measure different properties of the earth surface, including optical images, SAR images and hyperspectral images. One of the interest of this images is the detection of changes on datasets of multitemporal images. Change detection has been thoroughly studied on the case where the dataset consist of images acquired by the same sensor. However, having to deal with datasets containing images acquired from different sensors (heterogeneous images) is becoming very common nowadays. In order to deal with heterogeneous images, we proposed a statistical model which describe the joint distribution of the pixel intensity of the images, more precisely a mixture model. On unchanged areas, we expect the parameter vector of the model to belong to a manifold related to the physical properties of the objects present on the image, while on areas presenting changes this constraint is relaxed. The distance of the model parameter to the manifold can be thus be used as a similarity measure, and the manifold can be learned using ground truth images where no changes are present. The model parameters are estimated through a collapsed Gibbs sampler using a Bayesian non parametric approach combined with a Markov random field. In this talk I will present the proposed statistical model, its parameter estimation, and the manifold learning approach. The results obtained with this method will be compared with those of other classical similarity measures.

Biography — Jorge Prendes was born in Santa Fe, Argentina in 1987. He received the 5 years Eng. degree in Electronics Engineering with honours from the Buenos Aires Institute of Technology (ITBA), Buenos Aires, Argentina in July 2010. He worked on Signal Processing at ITBA within the Applied Digital Electronics Group (GEDA) from July 2010 to September 2012. Currently he is a Ph.D. student in Signal Processing in SONDRA laboratory at Supélec, within the cooperative laboratory TéSA and the Signal and Communication Group of the Institut de Recherche en Informatique de Toulouse (IRIT). His main research interest include image processing, applied mathematics and pattern recognition.

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S³ Seminar

March 20, 2015
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  1. Analysis of remote sensing multi-sensor heterogeneous images Jorge PRENDES Marie

    CHABERT, Fr´ ed´ eric PASCAL, Alain GIROS, Jean-Yves TOURNERET March 20, 2015 – S3 Seminar, Sup´ elec
  2. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Outline 1 Introduction 2 Image model 3 Similarity measure 4 Expectation maximization 5 Bayesian non parametric 6 Conclusions J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 2 / 41
  3. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Remote Sensing Images Remote sensing images are images of the Earth surface captured from a satellite or an airplane. J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 3 / 41
  4. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Change Detection Multitemporal datasets are groups of images acquired at different times. We can detect changes on them! J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 4 / 41
  5. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Heterogeneous Sensors Optical images are not the only kind of images captured. J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 5 / 41
  6. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Heterogeneous Sensors For instance, SAR images can be captured during the night, or with bad weather conditions. J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 6 / 41
  7. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Difference Image J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 7 / 41
  8. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Difference Image J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 7 / 41
  9. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Sliding window Optical SAR Images WOpt WSAR Sliding Window: W d = f(WOpt , WSAR ) Similarity Measure H0 : Absence of change H1 : Presence of change d H0 ≷ H1 τ Decision . . . Using several windows Result J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 8 / 41
  10. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Correlation coefficient d = f (W1 , W2) = E[(W1 − µW1 )(W2 − µW2 )] E (W1 − µW1 )2 E (W2 − µW2 )2  no change  change J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 9 / 41
  11. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Correlation coefficient J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 10 / 41
  12. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Correlation coefficient J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 10 / 41
  13. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Mutual information d = f (W1 , W2) = w1∈W1 w2∈W2 p(w1 , w2) log p(w1 , w2) p(w1)p(w2)  no change  change J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 11 / 41
  14. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Mutual information J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 12 / 41
  15. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Introduction Mutual information J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 12 / 41
  16. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Image model Optical image Affected by additive Gaussian noise IOpt = TOpt(P) + νN(0,σ2) IOpt|P ∼ N TOpt(P), σ2 where TOpt(P) is how an object with physical properties P would be ideally seen by an optical sensor σ2 is associated with the noise variance 0 1 0 5 10 IOpt Histogram of the normalized image J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 13 / 41
  17. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Image model SAR image Affected by multiplicative speckle noise (with gamma distribution) ISAR = TSAR(P) × ν Γ(L, 1 L ) ISAR|P ∼ Γ L, TSAR(P) L where TSAR(P) is how an object with physical properties P would be ideally seen by a SAR sensor L is the number of looks of the SAR sensor 0 1 0 2 4 ISAR Histogram of the normalized image J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 14 / 41
  18. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Image model Joint distribution Independence assumption for the sensor noises p(IOpt , ISAR|P) = p(IOpt|P) × p(ISAR|P) Conclusion Statistical dependency (CC, MI) is not always an appropriate similarity measure 0 1 0 1 IOpt ISAR J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 15 / 41
  19. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Image model Sliding window Usually includes a finite number of objects, K Different values of P for each object Pr(P = Pk|W ) = wk p(IOpt , ISAR|W ) = K k=1 wkp(IOpt , ISAR|Pk) Mixture distribution! 0 1 0 1 IOpt ISAR J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 16 / 41
  20. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Motivation Parameters of the mixture distribution Can be used to derive [TOpt(P), TSAR(P)] for each object IOpt P ∼ N TOpt(P), σ2 ISAR|P ∼ Γ L, TSAR(P) L Related to P They are not independent 0 1 0 1 IOpt ISAR 0 1 0 1 P1 P2 P3 P4 TOpt (P) TSAR (P) J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 17 / 41
  21. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Manifold For each unchanged window, v(P) = [TOpt(P), TSAR(P)] can be considered as a point on a manifold The manifold is parametric on P Estimating v(P) from pixels with different values of P will trace the manifold Several unchanged windows . . . 0 1 0 0.3 TOpt (P) TSAR (P) J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 18 / 41
  22. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Distance to the manifold Unchanged regions Pixels belong to the same object P is the same for both images ˆ v = ˆ TOpt(P), ˆ TSAR(P) 0 1 0 0.3 TOpt (P) TSAR (P)  Changed regions Pixels belong to different objects P changes from one image to another ˆ v = ˆ TOpt(P1), ˆ TSAR(P2) 0 1 0 0.3 TOpt (P) TSAR (P)  J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 19 / 41
  23. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Manifold estimation The manifold is a priori unknown We must estimating the distance to the manifold PDF of v(P) Good distance measure Learned using training data from unchanged images 0 1 0 0.3 TOpt (P) TSAR (P) → 0 1 0 0.3 TOpt (P) TSAR (P) J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 20 / 41
  24. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Manifold estimation The manifold is a priori unknown We must estimating the distance to the manifold PDF of v(P) Good distance measure Learned using training data from unchanged images 0 1 0 0.3 TOpt (P) TSAR (P) → 0 1 0 0.3 TOpt (P) TSAR (P) H0 : Absence of change H1 : Presence of change ˆ pv (ˆ v)−1 H1 ≷ H0 τ J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 20 / 41
  25. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Similarity measure Summary WOpt WSAR Sliding Window: W Mixture µ1 , σ2 1 , k1 , α1 θ1 : TS1 (P1), TS2 (P1) vP1 : µ4 , σ2 4 , k4 , α4 θ4 : TS1 (P4), TS2 (P4) vP4 : . . . . . . 0 1 0 0.3 P1 P2 P3 P4 TS1 (P) TS2 (P) Manifold Samples . . . 0 1 0 0.3 TOpt (P) TSAR (P) Using several windows 0 1 0 0.3 TOpt (P) TSAR (P) Manifold Estimation J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 21 / 41
  26. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Expectation maximization Motivation To estimate v(P) we must estimate the mixture parameters θ We can use a maximum likelihood estimator θ = arg max θ p(IOpt , ISAR|θ) Two pixels iOpt,n and iSAR,m are not independent J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 22 / 41
  27. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Expectation maximization Algorithm The class labels Z make the pixels independent p(IOpt , ISAR|θ, Z) = N n=1 p(iOpt,n , iSAR,n|θ, zn) where we have N pixels in the window Now we also have to estimate Z θ = arg max θ p(IOpt , ISAR|θ, Z) = N n=1 log [p(iOpt,n , iSAR,n|θ, zn)] Z can take NK different values J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 23 / 41
  28. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Expectation maximization Algorithm Iterative algorithm, estimate θ(i) using θ(i−1) p z(i) n = k = p iOpt,n, iSAR,n θ(i−1), zn = k K j=1 p iOpt,n, iSAR,n θ(i−1), zn = j θ(i) = N n=1 log   K j=1 p iOpt,n, iSAR,n θ(i−1), zn = j × p z(i) n = j   The value of K is fixed J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 24 / 41
  29. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Synthetic Optical and SAR Images Synthetic optical image Synthetic SAR image Change mask Mutual Information Correlation Coefficient Proposed Method 0 1 0 1 PFA PD Proposed Correlation Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 25 / 41
  30. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Real Optical and SAR Images Optical image before the flooding SAR image during the flooding Change mask [1] G. Mercier, G. Moser, and S. B. Serpico, “Conditional copulas for change detection in heterogeneous remote sensing images,” IEEE Trans. Geosci. and Remote Sensing, vol. 46, no. 5, pp. 1428–1441, May 2008. Mutual Information Conditional Copulas [1] Proposed Method 0 1 0 1 PFA PD Proposed Copulas Correlation Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 26 / 41
  31. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Pl´ eiades Images Pl´ eiades – May 2012 Pl´ eiades – Sept. 2013 Change mask Special thanks to CNES for providing the Pl´ eiades images Change map 0 1 0 1 PFA PD Proposed Correlation Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 27 / 41
  32. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Pl´ eiades and Google Earth Images Pl´ eiades – May 2012 Google Earth – July 2013 Change mask Change map 0 1 0 1 PFA PD Proposed Correlation Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 28 / 41
  33. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results Homogeneous images Pl´ eiades – Pl´ eiades 0 1 0 1 PFA PD Proposed Correlation Mutual Inf. CC and MI Similar performance Proposed method Improved performance Heterogeneous images Pl´ eiades – Google Earth 0 1 0 1 PFA PD Proposed Correlation Mutual Inf. CC Reduced Performance Proposed method and MI Performance not affected J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 29 / 41
  34. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Motivation Introduce a Bayesian framework into the labels: K is not fixed J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 30 / 41
  35. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Motivation Introduce a Bayesian framework into the labels: K is not fixed Classic mixture model in|vn ∼ F(vn) vn V ∼ K k=1 wk δ vn − vk in = iOpt,n, iSAR,n , and F is a distribution family which is application dependent, i.e., a bivariate Normal-Gamma distribution. J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 30 / 41
  36. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Motivation Prior in the mixture parameters vk ∼ V0 w ∼ Dir αK−1uK Now make K → ∞ vn will still present clustering behavior There are infinite parameters for the prior of vn J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 31 / 41
  37. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Bayesian non parametric Dirichlet Process in|vn ∼ F(vn) vn ∼ V V ∼ DP(V0 , α). in|zn ∼ F vzn z ∼ CRP(α) vk ∼ V0 . Algorithm For n ≥ 1 u ∼ Uniform(1, α + n) If u < n vn ← v u Else vn ∼ V0 J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 32 / 41
  38. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Markov random fields Markov random fields are a common tool to capture spatial correlation We would like to define p zn z\n = p zn zδ(n) MRF define the constraints to define a joint distribution p(Z) J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 33 / 41
  39. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Bayesian non parametric Markov random fields We will define out joint distribution as p zn z\n ∝ exp H zn z\n H zn z\n = Hn(zn) + m∈δ(n) ωnm 1zn (zm) = Hn(zn) + m∈δ(n) zn=zm ωnm The trick is to take Hn(zn) = log p(zn|In , V ) J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 34 / 41
  40. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Synthetic Optical and SAR Images Synthetic optical image Synthetic SAR image Change mask Mutual Information EM BNP 0 1 0 1 PFA PD BNP-MRF EM Correlaton Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 35 / 41
  41. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Real Optical and SAR Images Optical image before the flooding SAR image during the flooding Change mask Mutual Information EM BNP 0 1 0 1 PFA PD BNP-MRF EM Copulas Correlaton Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 36 / 41
  42. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Pl´ eiades Images Pl´ eiades – May 2012 Pl´ eiades – Sept. 2013 Change mask Special thanks to CNES for providing the Pl´ eiades images EM BNP 0 1 0 1 PFA PD BNP-MRF EM Correlaton Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 37 / 41
  43. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Results Results – Pl´ eiades and Google Earth Images Pl´ eiades – May 2012 Google Earth – July 2013 Change mask EM BNP 0 1 0 1 PFA PD BNP-MRF EM Correlaton Mutual Inf. Performance – ROC J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 38 / 41
  44. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Conclusions Conclusions and Future Work Conclusions New statistical model to describe multi-channel images Analyze the joint behavior of the channels to detect changes, in contrast with channel by channel analysis e.g., Pl´ eiades multi-spectral and panchromatic images New similarity measure showing encouraging results for homogeneous and heterogeneous sensors Pl´ eiades–Pl´ eiades Pl´ eiades – SAR Pl´ eiades – Other VHR instument Interesting for many applications Change detection Registration Segmentation Classification J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 39 / 41
  45. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Conclusions Conclusions and Future Work Future Work Study the method performance for different image features Texture coefficients: Haralick, Gabor, QMF Wavelet coefficients Gradients J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 40 / 41
  46. Introduction Image model Similarity measure Expectation maximization Bayesian non parametric

    Conclusions Conclusions Thank you for your attention Jorge Prendes jorge.prendes@tesa.prd.fr J. Prendes T´ eSA – Sup´ elec-SONDRA – INP/ENSEEIHT – CNES Analysis of remote sensing multi-sensor heterogeneous images 41 / 41