Advertisement

Change Detection in Multitemporal Images Through Single- and Multi-scale Approaches

  • Bruno Aiazzi
  • Francesca Bovolo
  • Lorenzo Bruzzone
  • Andrea Garzelli
  • Davide Pirrone
  • Claudia Zoppetti
Chapter
First Online:
Part of the Signals and Communication Technology book series (SCT)

Abstract

This chapter presents an analysis of the current status and the challenges in change detection techniques for the analysis of multitemporal SAR images. Algorithms and methods based on validated statistical models for SAR data are investigated, which adopt advanced information-theoretic and multi-scale signal-processing methodologies. After a brief review of the recent literature on general change detection methods, the chapter investigates the specific problem of change detection in SAR images. The main properties of the change detection problem in SAR images are explored and discussed. Then, recent change detection techniques for high-resolution (HR) and very high-resolution (VHR) SAR data are presented and critically analyzed from the theoretical viewpoint. Finally, examples of application of these techniques to real problems are presented by using simulated image pairs and Enhanced Spotlight COSMO-SkyMed images.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Achim, A., Kuruoglu, E.E., Zerubia, J.: SAR image filtering based on the heavy-tailed Rayleigh model. IEEE Trans. Image Process. 15(9), 2686–2693 (2006)CrossRefGoogle Scholar
  2. 2.
    Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., Nencini, F: Information-theoretic multitemporal features for change analysis from SAR images. In: Image and Signal Processing for Remote Sensing XIV, 7109, p. 71090S (2008)Google Scholar
  3. 3.
    Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., Zoppetti, C.: A robust change detection feature for COSMO-SkyMed detected SAR images. In: Proceedings of MultiTemp 2011, International Workshop on the Analysis of Multi-temporal Remote Sensing Images, pp. 125–128 (2011)Google Scholar
  4. 4.
    Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., Zoppetti, C.: Nonparametric change detection in multitemporal SAR images based on mean-shift clustering. IEEE Trans. Geosci. Remote Sens. 51(4), 2022–2031 (2013)CrossRefGoogle Scholar
  5. 5.
    Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A.D: An information-theoretic feature for multi-temporal analysis of SAR images. In: Proceedings of ESA-EUSC 2006: Image Information Mining for Security and Intelligence, ESA Workshop Proceedings Publication WPP-274, pp. 67–76 (2006)Google Scholar
  6. 6.
    Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., Nencini, F.: Robust change analysis of SAR data through information-theoretic multi temporal features. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 3883–3886 (2007)Google Scholar
  7. 7.
    Ban, Y., Yousif, O.A.: Multitemporal spaceborne SAR data for urban change detection in China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(4), 1087–1094 (2012)CrossRefGoogle Scholar
  8. 8.
    Bazi, Y., Bruzzone, L., Melgani, F.: An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci Remote Sens. 43(4), 874–887 (2005)CrossRefGoogle Scholar
  9. 9.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  10. 10.
    Bovolo, F.: A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geosci. Remote Sens. Lett. 6(1), 33–37 (2009)CrossRefGoogle Scholar
  11. 11.
    Bovolo, F., Bruzzone, L.: A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43(12), 2963–2972 (2005)CrossRefGoogle Scholar
  12. 12.
    Bovolo, F., Bruzzone, L.: A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans. Geosci. Remote Sens. 45(1), 218–236 (2007)CrossRefGoogle Scholar
  13. 13.
    Bovolo, F., Marchesi, S., Bruzzone, L.: A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Trans. Geosci. Remote Sens. 50(6), 2196–2212 (2012)CrossRefGoogle Scholar
  14. 14.
    Bovolo, F., Marin, C., Bruzzone, L.: A hierarchical approach to change detection in very high resolution SAR images for surveillance applications. IEEE Trans. Geosci. Remote Sens. 51(4), 2042–2054 (2013)CrossRefGoogle Scholar
  15. 15.
    Brunner, D., Lemoine, G., Bruzzone, L.: Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Trans. Geosci. Remote Sens. 48(5), 2403–2420 (2010)CrossRefGoogle Scholar
  16. 16.
    Bruzzone, L., Bovolo, F.: A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proc. IEEE 101(3), 609–630 (2013)CrossRefGoogle Scholar
  17. 17.
    Bruzzone, L., Cossu, R.: An adaptive approach to reducing registration noise effects in unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 41(11), 2455–2465 (2003)CrossRefGoogle Scholar
  18. 18.
    Bruzzone, L., Fernández Prieto, D.: A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 37(2), 1179–1184 (1999)CrossRefGoogle Scholar
  19. 19.
    Bruzzone, L., Fernández Prieto, D.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1171–1182 (2000)CrossRefGoogle Scholar
  20. 20.
    Bruzzone, L., Fernández Prieto, D.: Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 39(2), 456–460 (2001)CrossRefGoogle Scholar
  21. 21.
    Bruzzone, L., Fernández Prieto, D.: An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Trans. Image Process. 11(4), 452–466 (2002)CrossRefGoogle Scholar
  22. 22.
    Bruzzone, L., Serpico, S.B.: An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images. IEEE Trans. Geosci. Remote Sens. 35(4), 858–867 (1997)CrossRefGoogle Scholar
  23. 23.
    Bruzzone, L., Fernández Prieto, D., 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)CrossRefGoogle Scholar
  24. 24.
    Bruzzone, L., Cossu, R., Vernazza, G.: Detection of land-cover transitions by combining multidate classifiers. Pattern Recognit. Lett. 25(13), 1491–1500 (2004)CrossRefGoogle Scholar
  25. 25.
    Carincotte, C., Derrode, S., Bourennane, S.: Unsupervised change detection on SAR images using fuzzy hidden Markov chains. IEEE Trans. Geosci. Remote Sens. 44(2), 432–441 (2006)CrossRefGoogle Scholar
  26. 26.
    Celik, T., Ma, K.K.: Unsupervised change detection for satellite images using dual-tree complex wavelet transform. IEEE Trans. Geosci. Remote Sens. 48(3), 1199–1210 (2010)CrossRefGoogle Scholar
  27. 27.
    Celik, T., Ma, K.K.: Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Trans. Geosci. Remote Sens. 49(2), 706–716 (2011)CrossRefGoogle Scholar
  28. 28.
    Chen, J., Chen, X., Cui, X., Chen, J.: Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Trans. Geosci. Remote Sens. 8(2), 317–321 (2011)CrossRefGoogle Scholar
  29. 29.
    Chini, M., Pulvirenti, L., Pierdicca, N.: Analysis and interpretation of the COSMO-SkyMed observations of the 2011 Japan tsunami. IEEE Geosci. Remote Sens. Lett. 9(3), 467–471 (2012)CrossRefGoogle Scholar
  30. 30.
    Cihlar, J., Pultz, T.J., Gray, A.: Change detection with synthetic aperture radar. Int. J. Remote Sens. 13(3), 401–414 (1992)CrossRefGoogle Scholar
  31. 31.
    Cossu, R., Chaudhuri, S., Bruzzone, L.: A spatial-contextual partially supervised classifier based on Markov random fields. IEEE Geosci. Remote Sens. Lett. 352–356 (2005)Google Scholar
  32. 32.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley, New York (2006)zbMATHGoogle Scholar
  33. 33.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  34. 34.
    Cui, S., Datcu, M.: Statistical wavelet subband modeling for multi-temporal SAR change detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(4), 1095–1109 (2012)CrossRefGoogle Scholar
  35. 35.
    Dalla Mura, M., Benediktsson, J.A., Bovolo, F., Bruzzone, L.: An unsupervised technique based on morphological filters for change detection in very high resolution images. IEEE Geosci. Remote. Sens. Lett. 5(3), 433–437 (2008)CrossRefGoogle Scholar
  36. 36.
    Dekker, R.J.: Speckle filtering in satellite SAR change detection imagery. Int. J. Remote Sens. 19(6), 1133–1146 (1998)CrossRefGoogle Scholar
  37. 37.
    Dekker, R.J.: High-resolution radar damage assessment after the earthquake in Haiti on 12 january 2010. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(4), 960–970 (2011)CrossRefGoogle Scholar
  38. 38.
    Demir, B., Bovolo, F., Bruzzone, L.: Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification. IEEE Trans. Geosci. Remote Sens. 50(5), 1930–1941 (2012)CrossRefGoogle Scholar
  39. 39.
    Demir, B., Bovolo, F., Bruzzone, L.: Classification of time series of multispectral images with limited training data. IEEE Trans. Image Process. 22(8), 3219–3233 (2013)CrossRefGoogle Scholar
  40. 40.
    Demir, B., Bovolo, F., Bruzzone, L.: Updating land-cover maps by classification of image time series: a novel change-detection-driven transfer learning approach. IEEE Trans. Geosci. Remote Sens. 51(1), 300–312 (2013)CrossRefGoogle Scholar
  41. 41.
    Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)CrossRefGoogle Scholar
  42. 42.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)zbMATHGoogle Scholar
  43. 43.
    Erten, E., Reigber, A., Ferro-Famil, L., Hellwich, O.: A new coherent similarity measure for temporal multichannel scene characterization. IEEE Trans. Geosci. Remote Sens. 50(7), 2839–2851 (2012)CrossRefGoogle Scholar
  44. 44.
    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)CrossRefGoogle Scholar
  45. 45.
    Garzelli, A., Zoppetti, C.: A segmentation-based approach to SAR change detection and mapping. In: Proceedings of SPIE 10004, Image and Signal Processing for Remote Sensing XXII, pp. 1000, 410–1000, 410–10 (2016)Google Scholar
  46. 46.
    Gueguen, L., Soille, P., Pesaresi, M.: Change detection based on information measure. IEEE Trans. Geosci. Remote Sens. 49(11), 4503–4515 (2011)CrossRefGoogle Scholar
  47. 47.
    Hay, G.J., Castilla, G., Wulder, M.A., Ruiz, J.R.: An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Obs. Geoinf. 7(4), 339–359 (2005)CrossRefGoogle Scholar
  48. 48.
    Inglada, J., Mercier, G.: A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis. IEEE Trans. Geosci. Remote Sens. 45(5), 1432–1445 (2007)CrossRefGoogle Scholar
  49. 49.
    Jeon, B., Landgrebe, D.A.: Classification with spatio-temporal interpixel class dependency contexts. IEEE Trans. Geosci. Remote Sens. 30(4), 663–672 (1992)CrossRefGoogle Scholar
  50. 50.
    Klaric, M.N., Claywell, B.C., Scott, G.J., Hudson, N.J., Sjahputera, O., Li, Y., Barratt, S.T., Keller, J.M., Davis, C.H.: GeoCDX: an automated change detection and exploitation system for high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 51(4), 2067–2086 (2013)CrossRefGoogle Scholar
  51. 51.
    Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Upper Saddle River (1992)zbMATHGoogle Scholar
  52. 52.
    Li, S., Fang, L., Yin, H.: Multitemporal image change detection using a detail-enhancing approach with nonsubsampled contourlet transform. IEEE Geosci. Remote Sens. Lett. 9(5), 836–840 (2012)CrossRefGoogle Scholar
  53. 53.
    Liu, S., Bruzzone, L., Bovolo, F., Du, P.: Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Trans. Geosci. Remote Sens. 53(1), 244–260 (2015)CrossRefGoogle Scholar
  54. 54.
    Liu, S., Bruzzone, L., Bovolo, F., Zanetti, M., Du, P.: Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 53(8), 4363–4378 (2015)CrossRefGoogle Scholar
  55. 55.
    Marin, C., Bovolo, F., Bruzzone, L.: Building change detection in multitemporal very high resolution SAR images. IEEE Trans. Geosci. Remote Sens. 53(5), 2664–2682 (2015)CrossRefGoogle Scholar
  56. 56.
    Mason, D.C., Speck, R., Devereux, B., Schumann, G.J.P., Neal, J.C., Bates, P.D.: Flood detection in urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sens. 48(2), 882–894 (2010)CrossRefGoogle Scholar
  57. 57.
    Mercier, G., Moser, G., Serpico, S.B.: Conditional copulas for change detection in heterogeneous remote sensing images. IEEE Trans. Geosci. Remote Sens. 46(5), 1428–1441 (2008)CrossRefGoogle Scholar
  58. 58.
    Moser, G., Angiati, E., Serpico, S.B.: Multiscale unsupervised change detection on optical images by Markov random fields and wavelets. IEEE Geosci. Remote Sens. Lett. 8(4), 725–729 (2011)CrossRefGoogle Scholar
  59. 59.
    Nielsen, A.A.: The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data. IEEE Trans. Image Process. 16(2), 463–478 (2007)MathSciNetCrossRefGoogle Scholar
  60. 60.
    Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. SciTech Publishing (2004)Google Scholar
  61. 61.
    Richards, J.A.: Remote Sensing Digital Image Analysis, 5th edn. Springer, Berlin (2013)CrossRefGoogle Scholar
  62. 62.
    Rignot, E.J., van Zyl, J.J.: Change detection techniques for ERS-1 SAR data. IEEE Trans. Geosci. Remote Sens. 31(4), 896–906 (1993)CrossRefGoogle Scholar
  63. 63.
    Schmitt, A., Wessel, B., Roth, A.: An innovative curvelet-only-based approach for automated change detection in multi-temporal SAR imagery. MDPI Remote Sens. 6(3), 2435–2462 (2014)CrossRefGoogle Scholar
  64. 64.
    Serpico, S., Bruzzone, L.: Change detection (Ch. 15). In: Chen, C.H. (ed.) Information Processing for Remote Sensing, World Scientific, Singapore (1999)Google Scholar
  65. 65.
    Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRefGoogle Scholar
  66. 66.
    Solano Correa, Y.T., Bovolo, F., Bruzzone, L.D: Change detection in very high resolution multisensor optical images. In: Proceedings of SPIE 9244, Image and Signal Processing for Remote Sensing XX, pp. 924,410–924,410 (2014)Google Scholar
  67. 67.
    Solano Correa, Y.T., Bovolo, F., Bruzzone, L.: VHR time-series generation by prediction and fusion of multi-sensor images. In: Proceedings of IEEE IGARSS’15, pp. 3298–3301 (2015)Google Scholar
  68. 68.
    Solano Correa, Y.T., Bovolo, F., Bruzzone, L.: An approach to multiple change detection in multisensor VHR optical images based on iterative clustering. In: Proceedings of IEEE IGARSS’16, pp. 5149–5152 (2016)Google Scholar
  69. 69.
    Solberg, A.H.S., Taxt, T., Jain, A.K.: A Markov random field model for classification of multisource satellite imagery. IEEE Trans. Geosci. Remote Sens. 34(1), 100–113 (1996)CrossRefGoogle Scholar
  70. 70.
    Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)CrossRefzbMATHGoogle Scholar
  71. 71.
    Wang, F.: Fuzzy supervised classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 28(2), 194–201 (1990)MathSciNetCrossRefGoogle Scholar
  72. 72.
    Zanetti, M., Bovolo, F., Bruzzone, L.: Rayleigh-Rice mixture parameter estimation via EM algorithm for change detection in multispectral images. IEEE Trans. Image Process. 24(12), 5004–5016 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Bruno Aiazzi
    • 1
  • Francesca Bovolo
    • 2
  • Lorenzo Bruzzone
    • 3
  • Andrea Garzelli
    • 4
  • Davide Pirrone
    • 2
  • Claudia Zoppetti
    • 4
  1. 1.Institute of Applied Physics “Nello Carrara”National Research CouncilSesto Fiorentino, FirenzeItaly
  2. 2.Fondazione Bruno KesslerPovo, TrentoItaly
  3. 3.Department of Information Engineering and Computer ScienceUniversity of TrentoPovo, TrentoItaly
  4. 4.Department of Information Engineering and MathematicsUniversity of SienaSienaItaly

Personalised recommendations

Cite chapter

Buy options