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Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification

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Mathematical Models for Remote Sensing Image Processing

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Current and forthcoming sensor technologies and space missions are providing remote sensing scientists and practitioners with an increasing wealth and variety of data modalities. They encompass multisensor, multiresolution, multiscale, multitemporal, multipolarization, and multifrequency imagery. While they represent remarkable opportunities for the applications, they pose important challenges to the development of mathematical methods aimed at fusing the information conveyed by the input multisource data. In this framework, the present chapter continues the discussion of remote sensing data fusion, which was opened in the previous chapter. Here, the focus is on data fusion for image classification purposes. Both methodological issues of feature extraction and supervised classification are addressed. On both respects, the focus is on hierarchical image models rooted in graph theory. First, multilevel feature extraction is addressed through the latest advances in Mathematical Morphology and attribute profile theory with respect to component trees and trees of shapes. Then, joint supervised classification of multisensor, multiscale, multiresolution, and multitemporal imagery is formulated through hierarchical Markov random fields on quad-trees. Examples of experimental results with data from current VHR optical and SAR missions are shown and analysed.

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References

  1. Akcay, H.G., Aksoy, S.: Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Trans. Geosci. Remote Sens. 46(7), 2097–2111 (2008)

    Article  Google Scholar 

  2. Alonso-Gonzalez, A., Valero, S., Chanussot, J., Lopez-Martinez, C., Salembier, P.: Processing multidimensional SAR and hyperspectral images with binary partition tree. Proc. IEEE 101(3), 723–747 (2013)

    Article  Google Scholar 

  3. Amici, G., Dell’Acqua, F., Gamba, P., Pulina, G.: A comparison of fuzzy and neuro-fuzzy data fusion for flooded area mapping using SAR images. Int. J. Remote Sens. 25(20), 4425–4430 (2004)

    Article  Google Scholar 

  4. Atkinson, P.M., Aplin, P.: Spatial variation in land cover and choice of spatial resolution for remote sensing. Int. J. Remote Sens. 25(18), 3687–3702 (2004)

    Article  Google Scholar 

  5. Bakos, K., Gamba, P.: Hierarchical hybrid decision tree fusion of multiple hyperspectral data processing chains. IEEE Trans. Geosci. Remote Sens. 49(1), 388–394 (2011)

    Article  Google Scholar 

  6. Baum, L., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. pp. 164–171 (1970)

    Google Scholar 

  7. Benediktsson, J.A.: Classification of multisource and hyperspectral data based on decision fusion. IEEE Trans. Geosci. Remote Sens. 37(3), 1367–1377 (1999)

    Google Scholar 

  8. Benediktsson, J.A., Bruzzone, L., Chanussot, J., Dalla Mura, M., Salembier, P., Valero, S.: Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees. In: Mathematical Morphology and Its Applications to Image and Signal Processing, vol. 6671 LNCS, pp. 306–319. Springer, Berlin (2011)

    Google Scholar 

  9. Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data From urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005)

    Article  Google Scholar 

  10. Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)

    Article  Google Scholar 

  11. Bernabe, S., Marpu, P.R., Plaza, A., Mura, M.D., Benediktsson, J.A.: Spectral-spatial classification of multispectral images using kernel feature space representation. IEEE Geosci. Remote Sens. Lett. 11(1), 288–292 (2014)

    Article  Google Scholar 

  12. Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. Opt. Eng. 34, 433–481 (1993)

    Google Scholar 

  13. Bigdeli, B., Samadzadegan, F., Reinartz, P.: A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data. Int. J. Image Data Fusion 5(3), 196–209 (2014)

    Article  Google Scholar 

  14. Bishop, C.: Pattern Recognition And Machine Learning. Springer, Berlin (2006)

    MATH  Google Scholar 

  15. Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65(1), 2–16 (2010)

    Article  Google Scholar 

  16. Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., Tiede, D.: Geographic object-based image analysis - towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 87, 180–191 (2014)

    Article  Google Scholar 

  17. Bogdanov, A.: Neuroinspired architecture for robust classifier fusion of multisensor imagery. IEEE Trans. Geosci. Remote Sens. 46(5), 1467–1487 (2008)

    Article  Google Scholar 

  18. Boudaren, M.E.Y., An, L., Pieczynski, W.: Dempster-Shafer fusion of evidential pairwise Markov fields. Int. J. Approx. Reason. 74, 13–29 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  19. Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Process. 3(2), 162–177 (1994)

    Article  Google Scholar 

  20. Breen, E.J., Jones, R.: Attribute openings, thinnings, and granulometries. Comput. Vis. Image Und. 64(3), 377–389 (1996)

    Article  Google Scholar 

  21. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  22. Bruzzone, L., Prieto, D.F., Serpico, S.B.: A neural-statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Trans. Geosci. Remote Sens. 37(3), 1350–1359 (1999)

    Article  Google Scholar 

  23. Burnett, C., Blaschke, T.: A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol. Model. 168(3), 233–249 (2003)

    Article  Google Scholar 

  24. Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., Rojo-Alvarez, J., Martinez-Ramon, M.: Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans. Geosci. Remote Sens. 46(6), 1822–1835 (2008)

    Article  Google Scholar 

  25. Camps-Valls, G., Tuia, D., Bruzzone, L., Benediktsson, J.A.: Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Process. Mag. 31(1), 45–54 (2014)

    Article  Google Scholar 

  26. Carlinet, E., Géraud, T.: A comparative review of component tree computation algorithms. IEEE Trans. Image Process. 23(9), 3885–3895 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Caselles, V., Coll, B., Morel, J.M.: Topographic maps and local contrast changes in natural images. Int. J. Comput. Vision 33(1), 5–27 (1999)

    Article  Google Scholar 

  28. Caselles, V., Monasse, P.: Geometric Description Of Images As Topographic Maps, 1st edn. Springer, Berlin (1984)

    MATH  Google Scholar 

  29. Cavallaro, G., Falco, N., Dalla Mura, M., and J. A. Benediktsson.: Automatic Attribute Profiles. IEEE Trans. Image Process. 26(4), 1859–1872 (Apr 2017)

    Google Scholar 

  30. Cavallaro, G., Dalla Mura, M., Benediktsson, J. A., Bruzzone, L.: Extended self-dual attribute profiles for the classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 99(8), 1–5 (2015)

    Google Scholar 

  31. Ceamanos, X., Waske, B., Benediktsson, J.A., Chanussot, J., Fauvel, M., Sveinsson, J.: A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data. Int. J. Image Data Fusion 1(4), 293–307 (2010)

    Article  Google Scholar 

  32. Celeux, G., Chauveau, D., Diebolt, J.: Stochastic versions of the EM algorithm: an experimental study in the mixture case. J. Stat. Comput. Sim. 55(4), 287–314 (1996)

    Article  MATH  Google Scholar 

  33. Chanussot, J., Mauris, G., Lambert, P.: Fuzzy fusion techniques for linear features detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 37(3 I), 1292–1305 (1999)

    Google Scholar 

  34. Coburn, C.A., Roberts, A.C.B.: A multiscale texture analysis procedure for improved forest stand classification. Int. J. Remote Sens. 25(20), 4287–4308 (2004)

    Article  Google Scholar 

  35. Crozet, S., Géraud, T.: A first parallel algorithm to compute the morphological tree of shapes of nD Images. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2933–2937 (2014)

    Google Scholar 

  36. Dalla Mura, M., Benediktsson, J.A., Bruzzone, L.: Classification of hyperspectral images with extended attribute profiles and feature extraction techniques. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 76–79 (2010)

    Google Scholar 

  37. Dalla Mura, M., Benediktsson, J.A., Bruzzone, L.: Self-dual attribute profiles for the analysis of remote sensing images. In: Mathematical Morphology and Its Applications to Image and Signal Processing, pp. 320–330. Springer, Berlin (2011)

    Google Scholar 

  38. Dalla Mura, M., Benediktsson, J.A., Chanussot, J., Bruzzone, L.: The evolution of the morphological profile: From panchromatic to hyperspectral images. In: Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, pp. 123–146. Springer, Berlin (2011)

    Google Scholar 

  39. Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int. J. Remote Sens. 31(22), 5975–5991 (2010)

    Article  Google Scholar 

  40. Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 48(10), 3747–3762 (2010)

    Article  Google Scholar 

  41. Dalla Mura, M., Villa, A., Benediktsson, J.A., Chanussot, J., Bruzzone, L.: Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geosci. Remote Sens. Lett. 8(3), 542–546 (2011)

    Article  Google Scholar 

  42. Dalponte, M., Bruzzone, L., Gianelle, D.: Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Trans. Geosci. Remote Sens. 46(5), 1416–1427 (2008)

    Article  Google Scholar 

  43. Datcu, M., Melgani, F., Piardi, A., Serpico, S.B.: Multisource data classification with dependence trees. IEEE Trans. Geosci. Remote Sens. 40(3), 609–617 (2002)

    Article  Google Scholar 

  44. Dawid, A.: Applications of a general propagation algorithm for probabilistic expert systems. Stat. Comput. 2(1), 25–36 (1992)

    Article  Google Scholar 

  45. Dell’Acqua, F., Gamba, P.: Discriminating urban environments using multiscale texture and multiple SAR images. Int. J. Remote Sens. 27(18), 3797–3812 (2006)

    Article  Google Scholar 

  46. Demir, B., Bruzzone, L.: Histogram-based attribute profiles for classification of very high resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(4), 2096–2107 (2016)

    Article  Google Scholar 

  47. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser.B 39(1), 1–38 (1977)

    Google Scholar 

  48. Dos Santos, J., Gosselin, P.H., Philipp-Foliguet, S., Torres, Da S.R., Falcao, A.: Multiscale classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 50(10), 3764–3775 (2012)

    Google Scholar 

  49. El-melegy, M., Ahmed, S.: Neural networks in multiple classifier systems for remote-sensing image classification. Stud. Fuzziness Soft Comput. 210, 65–94 (2007)

    Article  Google Scholar 

  50. Falco, N., Benediktsson, J.A., Bruzzone, L.: Spectral and spatial classification of hyperspectral images Based on ICA and reduced morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(11), 6223–6240 (2015)

    Article  Google Scholar 

  51. Falco, N., Dalla Mura, M., Bovolo, F., Benediktsson, J.A., Bruzzone, L.: Change detection in VHR images based on morphological attribute profiles. IEEE Geosci. Remote Sens. Lett. 10(3), 636–640 (2013)

    Article  Google Scholar 

  52. Fauvel, M., Chanussot, J., Benediktsson, J.A.: Decision fusion for the classification of urban remote sensing images. IEEE Trans. Geosci. Remote Sens. 44(10), 2828–2838 (2006)

    Article  Google Scholar 

  53. Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)

    Article  Google Scholar 

  54. Forney, G.D.: The Viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)

    Article  MathSciNet  Google Scholar 

  55. Foucher, S., Bénié, G.B., Boucher, J.M.: Multiscale MAP filtering of SAR images. IEEE Trans. Image Process. 10(1), 49–60 (2001)

    Article  MATH  Google Scholar 

  56. Franchi, G., Angulo, J.: Morphological principal component analysis for hyperspectral image analysis. ISPRS Int. J. Geo-Inf. 5(6), 83 (2016)

    Article  Google Scholar 

  57. Gamba, P., Houshmand, B.: An efficient neural classification chain of SAR and optical urban images. Int. J. Remote Sens. 22(8), 1535–1553 (2001)

    Article  Google Scholar 

  58. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)

    Article  MATH  Google Scholar 

  59. Géraud, T., Carlinet, E., Crozet, S., Najman, L.: A quasi-linear algorithm to compute the tree of shapes of nD images. In: Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 98–110. Springer, Berlin (2013)

    Google Scholar 

  60. Gerke, M., Xiao, J.: Fusion of airborne laser scanning point clouds and images for supervised and unsupervised scene classification. ISPRS J. Photogramm. Remote Sens. 87, 78–92 (2014)

    Article  Google Scholar 

  61. Gomez-Chova, L., Tuia, D., Moser, G., Camps-Valls, G.: Multimodal classification of remote sensing images: A review and future directions. Proc. IEEE 103(9), 1560–1584 (2015)

    Article  Google Scholar 

  62. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  63. Hedhli, I., Moser, G., Serpico, S.B., Zerubia, J.: New hierarchical joint classification method of SAR-optical multiresolution remote sensing data. In: Proceedings of the IEEE European Signal Processing Conference, pp. 759–763 (2015)

    Google Scholar 

  64. Hedhli, I., Moser, G., Serpico, S.B., Zerubia, J.: A new cascade model for the hierarchical joint classification of multitemporal and multiresolution remote sensing data. IEEE Trans. Geosci. Remote Sens. 54(11), 6333–6348 (2016)

    Article  Google Scholar 

  65. Hedhli, I., Moser, G., Zerubia, J., Serpico, S.B.: New cascade model for hierarchical joint classification of multitemporal, multiresolution and multisensor remote sensing data. In: Proceedings of the IEEE International Conference on Image Processing, pp. 5247–5251 (2014)

    Google Scholar 

  66. Hoberg, T., Rottensteiner, F., Feitosa, R., Heipke, C.: Conditional random fields for multitemporal and multiscale classification of optical satellite imagery. IEEE Trans. Geosci. Remote Sens. 53(2), 659–673 (2015)

    Article  Google Scholar 

  67. Jalobeanu, A., Blanc-Feraud, L., Zerubia, J.: Satellite image deblurring using complex wavelet packets. Int. J. Comput. Vision 51(3), 205–217 (2003)

    Article  Google Scholar 

  68. Jones, R.: Component trees for image filtering and segmentation. In: Proceedings of the IEEE Workshop on Nonlinear Signal and Image Processing. Mackinac Island (1997)

    Google Scholar 

  69. Jones, R.: Connected filtering and segmentation using component trees. Comput. Vis. Image Und. 75(3), 215–228 (1999)

    Article  Google Scholar 

  70. Kato, Z., Zerubia, J.: Markov random fields in image segmentation. Found. Trends Signal Proc. 5(1–2), 1–155 (2012)

    MATH  Google Scholar 

  71. Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7, 48–50 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  72. Krylov, V., Moser, G., Serpico, S.B., Zerubia, J.: Supervised high-resolution dual-polarization SAR image classification by finite mixtures and copulas. IEEE J. Sel. Top. Signal Process. 5(3), 554–566 (2011)

    Article  Google Scholar 

  73. Krylov, V., Moser, G., Serpico, S.B., Zerubia, J.: On the method of logarithmic cumulants for parametric probability density function estimation. IEEE Trans. Image Process. 22(10), 3791–3806 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  74. Krylov, V., Moser, G., Serpico, S.B., Zerubia, J.: Enhanced dictionary-based SAR amplitude distribution estimation and its validation with very high-resolution data. IEEE Geosci. Remote Sens. Lett. 8(1), 148–152 (2011)

    Article  Google Scholar 

  75. Laferté, J.M., Pérez, P., Heitz, F.: Discrete Markov image modeling and inference on the quadtree. IEEE Trans. Image Process. 9(3), 390–404 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  76. Landgrebe, D.A.: Signal theory methods in multispectral remote sensing. John Wiley & Sons Inc., (2003)

    Google Scholar 

  77. Le Hegarat-Mascle, S., Richard, D., Ottle, C.: Multi-scale data fusion using Dempster-Shafer evidence theory. Integr. Comput.-Aid. Eng. 10(1), 9–22 (2003)

    Google Scholar 

  78. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  79. Lee, S., Crawford, M.M.: Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure. IEEE Trans. Image Process. 14(3), 312–320 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  80. Lemire, D.: A better alternative to piecewise linear time series segmentation. 2007, 545–550 (2006). arXiv:cs/0605103v8

  81. Li, M., Zang, S., Zhang, B., Li, S., Wu, C.: A review of remote sensing image classification techniques: The role of spatio-contextual information. Eur. J. Remote Sens. 47(1), 389–411 (2014)

    Article  Google Scholar 

  82. Li, S.: Markov Random Field Modeling In Image Analysis, 3rd edn. Springer, Berlin (2009)

    MATH  Google Scholar 

  83. Liao, W., Pizurica, A., Bellens, R., Gautama, S., Philips, W.: Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features. IEEE Geosci. Remote Sens. Lett. 12(3), 552–556 (2014)

    Article  Google Scholar 

  84. Liu, Z.G., Mercier, G., Dezert, J., Pan, Q.: Change detection in heterogeneous remote sensing images based on multidimensional evidential reasoning. IEEE Geosci. Remote Sens. Lett. 11(1), 168–172 (2014)

    Article  Google Scholar 

  85. Lombardo, P., Oliver, C., Pellizzeri, T., Meloni, M.: A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images. IEEE Trans. Geosci. Remote Sens. 41(11), 2500–2518 (2003)

    Article  Google Scholar 

  86. Loncan, L., De Almeida, L., Bioucas-Dias, J., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G., Simoes, M., Tourneret, J.Y., Veganzones, M., Vivone, G., Wei, Q., Yokoya, N.: Hyperspectral pansharpening: a review. IEEE Geosci. Remote Sens. Mag. 3(3), 27–46 (2015)

    Article  Google Scholar 

  87. Luettgen, M., Karl, W., Willsky, A.: Efficient multiscale regularization with applications to the computation of optical flow. IEEE Trans. Image Process. 3(1), 41–64 (1994)

    Article  Google Scholar 

  88. Willsky, A.: Multiresolution Markov models for signal and image processing. Proc. IEEE 90(8), 1396–1458 (2002)

    Article  Google Scholar 

  89. Luus, F., Salmon, B., Van Den Bergh, F., Maharaj, B.: Multiview deep learning for land-use classification. IEEE Geosci. Remote Sens. Lett. 12(12), 2448–2452 (2015)

    Article  Google Scholar 

  90. Mahmood, Z., Thoonen, G., Scheunders, P.: Automatic threshold selection for morphological attribute profiles. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 4946–4949 (2012)

    Google Scholar 

  91. Mallat, S.: A Wavelet Tour Of Signal Processing, 3rd edn. Academic press, Dublin (2008)

    Google Scholar 

  92. Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 701–716 (1989)

    Article  MATH  Google Scholar 

  93. Marceau, D.J.: The scale issue in social and natural sciences. Can. J. Remote Sens. 25(July), 347–356 (1999)

    Article  MathSciNet  Google Scholar 

  94. Marmanis, D., Datcu, M., Esch, T., Stilla, U.: Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci. Remote Sens. Lett. 13(1), 105–109 (2016)

    Article  Google Scholar 

  95. Marpu, P.R., Pedergnana, M., Dalla Mura, M., Benediktsson, J.A., Bruzzone, L.: Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci. Remote Sens. Lett. 10(2), 293–297 (2013)

    Article  Google Scholar 

  96. Marpu, P.R., Pedergnana, M., Dalla Mura, M., Peeters, S., Benediktsson, J.A., Bruzzone, L.: Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques. Int. J. Image Data Fusion 3(3), 269–298 (2012)

    Article  Google Scholar 

  97. Matheron, G.: Random Sets And Integral Geometry. John Wiley & Sons, Newyork (1975)

    Google Scholar 

  98. Melgani, F., Serpico, S.B.: A Markov random field approach to spatio-temporal contextual image classification. IEEE Trans. Geosci. Remote Sens. 41(11), 2478–2487 (2003)

    Article  Google Scholar 

  99. Melgani, F., Serpico, S.B., Vernazza, G.: Fusion of multitemporal contextual information by neural networks for multisensor remote sensing image classification. Integr. Comput.-Aid. Eng. 10(1), 81–90 (2003)

    Google Scholar 

  100. Merentitis, A., Debes, C.: Many hands make light work - on ensemble learning techniques for data fusion in remote sensing. IEEE Geosci. Remote Sens. Mag. 3(3), 86–99 (2015)

    Article  Google Scholar 

  101. Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Trans. Image Process. 9(5), 860–872 (2000)

    Article  Google Scholar 

  102. Moser, G., De Giorgi, A., Serpico, S.B.: Multiresolution supervised classification of panchromatic and multispectral images by Markov random fields and graph cuts. IEEE Trans. Geosci. Remote Sens. 43(8), 1901–1911 (2016)

    Google Scholar 

  103. Moser, G., Serpico, S.B., Benediktsson, J.A.: Land-cover mapping by Markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proc. IEEE 101(3), 631–651 (2013)

    Article  Google Scholar 

  104. Najman, L., Cousty, J.: A graph-based mathematical morphology reader. Pattern Recogn. Lett. 47, 3–17 (2014)

    Article  Google Scholar 

  105. Najman, L., Talbot, H.: Connected operators based on tree pruning strategies. In: Mathematical Morphology: From Theory to Applications, pp. 177–198. John Wiley & Sons, Newyork (2010)

    Google Scholar 

  106. Nishii, R.: A Markov random field-based approach to decision-level fusion for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 41(10), 2316–2319 (2003)

    Article  Google Scholar 

  107. Ouzounis, G.K., Pesaresi, M., Soille, P.: Differential area profiles: decomposition properties and efficient computation. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1533–1548 (2012)

    Article  Google Scholar 

  108. Ouzounis, G.K., Soille, P.: The Alpha-tree Algorithm. Publications Office of the European Union, EUR 25500 EN (2012)

    Google Scholar 

  109. Ouzounis, G.K., Wilkinson, M.H.F.: Partition-induced connections and operators for pattern analysis. Pattern Recogn. 43(10), 3193–3207 (2010)

    Article  MATH  Google Scholar 

  110. Pacifici, F., Chini, M., Emery, W.J.: A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens. Environ. 113(6), 1276–1292 (2009)

    Article  Google Scholar 

  111. Palau, A., Melgani, F., Serpico, S.B.: Cell algorithms with data inflation for non-parametric classification. Pattern Recogn. Lett. 27(7), 781–790 (2006)

    Article  Google Scholar 

  112. Park, N.W., Moon, W., Chi, K.H., Kwon, B.D.: Multi-sensor data fusion for supervised land-cover classification using Bayesian and geostatistical techniques. Geosci. J. 6(3) (2002)

    Google Scholar 

  113. Pedergnana, M., Marpu, P.R., Dalla Mura, M., Benediktsson, J.A., Bruzzone, L.: Classification of remote sensing optical and LiDAR data using extended attribute profiles. IEEE J. Sel. Top. Signal Process. 6(7), 856–865 (2012)

    Article  Google Scholar 

  114. Peeters, S., Marpu, P.R., Benediktsson, J.A., Dalla Mura, M.: Classification using extended morphological attribute profiles based on different feature extraction techniques. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 4453–4456 (2011)

    Google Scholar 

  115. Pérez, P., Chardin, A., Laferté, J.M.: Noniterative manipulation of discrete energy-based models for image analysis. Pattern Recogn. 33(4), 573–586 (2000)

    Article  Google Scholar 

  116. Pesaresi, M., Benediktsson, J.A.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosc. Remote Sens. 39(2), 309–320 (2001)

    Article  Google Scholar 

  117. Plaza, A., Martinez, P., Plaza, J., Perez, R.: Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans. Geosci. Remote Sens. 43(3), 466–479 (2005)

    Article  Google Scholar 

  118. Poggi, G., Scarpa, G., Zerubia, J.: Supervised segmentation of remote sensing images based on a tree-structured MRF model. IEEE Trans. Geosci. Remote Sens. 54(9), 5054–5070 (2005)

    Google Scholar 

  119. Pohl, C., van Genderen, J.: Remote sensing image fusion: An update in the context of digital Earth. Int. J. Digital Earth 7(2), 158–172 (2014)

    Article  Google Scholar 

  120. Quesada-Barriuso, P., Arguello, F., Heras, D.B.: Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(4), 1177–1185 (2014)

    Google Scholar 

  121. Ran, Y., Li, X., Lu, L., Li, Z.: Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory. Int. J. Geogr. Inf. Sci. 26(1), 169–191 (2012)

    Article  Google Scholar 

  122. Ranchin, T., Wald, L.: The wavelet transform for the analysis of remotely sensed images. Int. J. Remote Sens. 14(3), 615–619 (1993)

    Article  Google Scholar 

  123. Saeidi, V., Pradhan, B., Idrees, M., Latif, Z.: Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using Dempster-Shafer theory. IEEE Trans. Geosci. Remote Sens. 52(10), 6017–6025 (2014)

    Article  Google Scholar 

  124. Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. Image Process. 9(4), 561–576 (2000)

    Article  Google Scholar 

  125. Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)

    Article  Google Scholar 

  126. Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Trans. Image Process. 4(8), 1153–1160 (1995)

    Article  Google Scholar 

  127. Salembier, P., Wilkinson, M.: Connected operators. IEEE Signal Process. Mag. 26(6), 136–157 (2009)

    Article  Google Scholar 

  128. Scarpa, G., Gaetano, R., Haindl, M., Zerubia, J.: Hierarchical multiple Markov chain model for unsupervised texture segmentation. IEEE Trans. Image Process. 18(8), 1830–1843 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  129. Schistad Solberg, A., Taxt, T., Jain, A.: A Markov random field model for classification of multisource satellite imagery. IEEE Trans. Geosci. Remote Sens. 34(1), 100–113 (1996)

    Article  Google Scholar 

  130. Serra, J.: Image Analysis And Mathematical Morphology. Academic Press, Dublin (1982)

    Google Scholar 

  131. Serra, J.: Image Analysis and Mathematical Morphology. Theoretical Advances. Serra, J. (ed.), vol. 2. Journal of Microscopy (1988)

    Google Scholar 

  132. Simard, M., Saatchi, S.S., De Grandi, G.: The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest. IEEE Trans. Geosci. Remote Sens. 38(5), 2310–2321 (2000)

    Google Scholar 

  133. Soille, P.: Morphological Image Analysis: Principles And Applications, 2nd edn. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  134. Song, B., Dalla Mura, M., Li, P., Plaza, A.J., Bioucas-Dias, J.M., Benediktsson, J.A., Chanussot, J.: Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 52(8), 5122–5136 (2014)

    Article  Google Scholar 

  135. Song, Y.: A Topdown algorithm for computation of level line trees. IEEE Trans. Image Process. 16(8), 2107–2116 (2007)

    Article  MathSciNet  Google Scholar 

  136. Storvik, B., Storvik, G., Fjortoft, R.: On the combination of multisensor data using meta-Gaussian distributions. IEEE Trans. Geosci. Remote Sens. 47(7), 2372–2379 (2009)

    Article  Google Scholar 

  137. Sutton, C., McCallum, A.: An introduction to conditional random fields. Found. Trends Mach. Learn. 4(4), 267–373 (2011)

    Article  MATH  Google Scholar 

  138. Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Multiple spectral-spatial classification approach for hyperspectral data. IEEE Trans. Geosci. Remote Sens. 48(11), 4122–4132 (2010)

    Google Scholar 

  139. Thoonen, G., Mahmood, Z., Peeters, S., Scheunders, P.: Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 510–521 (2012)

    Google Scholar 

  140. Tilton, J.C.: Analysis of hierarchically related image segmentations. In: Proceedings of the 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data 00(C), 60–69 (2004)

    Google Scholar 

  141. Tuceryan, M., Jain, A.K.: Texture analysis. In: The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific (1998)

    Google Scholar 

  142. Tuia, D., Flamary, R., Courty, N.: Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions. ISPRS J. Photogramm. Remote Sens. 105, 272–285 (2015)

    Article  Google Scholar 

  143. Tuia, D., Moser, G.: Foreword to the special issue on data fusion in remote sensing. IEEE Geosci. Remote Sens. Mag. 3(3), 6–7 (2015)

    Article  Google Scholar 

  144. Tuia, D., Pacifici, F., Kanevski, M., Emery, W.: Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans. Geosci. Remote Sens. 47(11), 3866–3879 (2009)

    Article  Google Scholar 

  145. Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 272–285 (2007)

    Article  Google Scholar 

  146. Urbach, E.R., Wilkinson, M.H.F.: Shape-only granulometries and grey-scale shape filters. In: Mathematical Morphology and Its Application to Signal and Image Processing - Proceedings of the 6th International Symposium on Mathematical Morphology, vol. 6, pp. 305–314 (2002)

    Google Scholar 

  147. Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Trans. Image Process. 22(4), 1430–1443 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  148. Velasco-Forero, S., Angulo, J.: Classification of hyperspectral images by tensor modeling and additive morphological decomposition. Pattern Recogn. 46(2), 566–577 (2013)

    Article  MATH  Google Scholar 

  149. Voisin, A., Krylov, V., Moser, G., Serpico, S.B., Zerubia, J.: Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach. IEEE Trans. Geosci. Remote Sens. 52(6), 3346–3358 (2014)

    Article  Google Scholar 

  150. Waske, B., Van Der Linden, S.: Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Trans. Geosci. Remote Sens. 46(5), 1457–1466 (2008)

    Article  Google Scholar 

  151. Wu, J., Jiang, Z., Luo, J., Zhang, H.: Composite kernels conditional random fields for remote-sensing image classification. Electron. Lett. 50(22), 1589–1591 (2014)

    Article  Google Scholar 

  152. Xia, J., Dalla Mura, M., Chanussot, J., Du, P., He, X.: Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 53(9), 4768–4786 (2015)

    Article  Google Scholar 

  153. Xia, J., Liao, W., Chanussot, J., Du, P., Song, G., Philips, W.: Improving random forest with ensemble of features and semisupervised feature extraction. IEEE Geosci. Remote Sens. Lett. 12(7), 1471–1475 (2015)

    Article  Google Scholar 

  154. Xu, Y., Carlinet, E., Géraud, T., Najman, L.: Efficient computation of attributes and saliency maps on tree-based image representations. In: Mathematical Morphology and Its Application to Signal and Image Processing - Proceedings of the 12th International Symposium on Mathematical Morphology, vol. 9082, pp. 693–704. Springer, Berlin (2015)

    Google Scholar 

  155. Xu, Y., Géraud, T., Najman, L.: Morphological filtering in shape spaces: applications using tree-based image representations. Proceedings of the 21st International Conference on Pattern Recognition 5, 2–5 (2012)

    Google Scholar 

  156. Zhang, Y., Yang, H., Prasad, S., Pasolli, E., Jung, J., Crawford, M.: Ensemble multiple kernel active learning for classification of multisource remote sensing data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(2), 845–858 (2015)

    Google Scholar 

  157. Zhang, Z., Pasolli, E., Crawford, M.M., Tilton, J.C.: An active learning framework for hyperspectral image classification using hierarchical segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(2), 640–654 (2016)

    Google Scholar 

  158. Zhao, W., Guo, Z., Yue, J., Zhang, X., Luo, L.: On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. Int. J. Remote Sens. 36(13), 3368–3379 (2015)

    Article  Google Scholar 

  159. Zhong, Z., Fan, B., Duan, J., Wang, L., Ding, K., Xiang, S., Pan, C.: Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 12(5), 1028–1032 (2015)

    Article  Google Scholar 

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Acknowledgements

This work was partly supported by the French Space Agency (Centre National d’Etudes Spatiales, CNES) through contract no. 8361. The authors would like to thank CNES, the Italian Space Agency (ASI), and GeoEye Inc. and Google Crisis Response for providing the Pléiades, COSMO-SkyMed, and GeoEye imagery used for experiments.

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Authors and Affiliations

  1. University of Iceland, Saemundargotu 2 - 101, Reykjavik, Iceland

    Jon A. Benediktsson & Gabriele Cavallaro

  2. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA

    Nicola Falco

  3. University of Genoa, Via Opera Pia 11a, 16145, Genoa, Italy

    Ihsen Hedhli, Vladimir A. Krylov, Gabriele Moser & Sebastiano B. Serpico

  4. Université Côte d’Azur, Inria, BP 93, 2004, Route des Lucioles, 06902, Sophia Antipolis Cedex, France

    Ihsen Hedhli, Vladimir A. Krylov & Josiane Zerubia

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    Gabriele Moser

  2. Université Côte d’Azur, Inria, Sophia Antipolis Cedex, France

    Josiane Zerubia

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Benediktsson, J.A. et al. (2018). Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification. In: Moser, G., Zerubia, J. (eds) Mathematical Models for Remote Sensing Image Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-66330-2_7

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