Skip to main content
SpringerLink
Log in

Hierarchical Analysis of Remote Sensing Data: Morphological Attribute Profiles and Binary Partition Trees

  • Conference paper
Mathematical Morphology and Its Applications to Image and Signal Processing (ISMM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6671))

Abstract

The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrow’s challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Daya Sagar, B.S., Serra, J.: Spatial information retrieval, analysis, reasoning and modelling. International Journal of Remote Sensing 31(22), 5747–5750 (2010)

    Article  Google Scholar 

  2. Richards, J.A., Jia, X.: Remote sensing digital image analysis: an introduction. Springer, Heidelberg (2006)

    Google Scholar 

  3. Miller, H., Han, J.: Geographic data mining and knowledge discovery. Chapman & Hall/CRC data mining and knowledge discovery series. CRC Press, Boca Raton (2009)

    Google Scholar 

  4. Jhung, Y., Swain, P.: Bayesian contextual classification based on modified m-estimates and markov random fields. IEEE Transactions on Geoscience and Remote Sensing 34(1), 67–75 (1996)

    Article  Google Scholar 

  5. Datcu, M., Seidel, K., Walessa, M.: Spatial information retrieval from remote-sensing images. i. information theoretical perspective. IEEE Transactions on Geoscience and Remote Sensing 36(5), 1431–1445 (1998)

    Article  Google Scholar 

  6. Melgani, F., Serpico, S.: A markov random field approach to spatio-temporal contextual image classification. IEEE Transactions on Geoscience and Remote Sensing 41(11), 2478–2487 (2003)

    Article  Google Scholar 

  7. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)

    Article  Google Scholar 

  8. Jong, S., Meer, F.: Remote sensing image analysis: including the spatial domain. In: Remote Sensing and Digital Image Processing, vol. 1. Kluwer Academic, Dordrecht (2004)

    Google Scholar 

  9. Pesaresi, M., Benediktsson, J.A.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing 39(2), 309–320 (2001)

    Article  Google Scholar 

  10. Bruzzone, L., Carlin, L.: A multilevel context-based system for classification of very high spatial resolution images. IEEE Transactions on Geoscience and Remote Sensing 44, 2587–2600 (2006)

    Article  Google Scholar 

  11. Tarabalka, Y., Benediktsson, J., Chanussot, J., Tilton, J.: Multiple spectral-spatial classification approach for hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4122–4132 (2010)

    Google Scholar 

  12. Tarabalka, Y., Benediktsson, J.A., Chanussot, J.: Spectral & spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Transactions on Geoscience and Remote Sensing 47(8), 2973–2987 (2009)

    Article  Google Scholar 

  13. Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics 40(5), 1267–1279 (2010)

    Article  MATH  Google Scholar 

  14. Tarabalka, Y., Chanussot, J., Benediktsson, J.A., Angulo, J., Fauvel, M.: Segmentation and classification of hyperspectral data using watershed. In: Proc. IEEE International Geoscience and Remote Sensing Symposium 2008, IGARSS 2008, July 7-11, vol. 3, pp. III–652–III–655 (2008)

    Google Scholar 

  15. Gaetano, R., Scarpa, G., Poggi, G.: Hierarchical texture-based segmentation of multiresolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 47(7), 2129–2141 (2009)

    Article  Google Scholar 

  16. Navulur, K.: Multispectral Image Analysis Using the Object-Oriented Paradigm. CRC Press, Inc., Boca Raton (2006)

    Book  Google Scholar 

  17. Blaschke, T., Lang, S., Hay, G.: Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Lecture notes in geoinformation and cartography. Springer, Heidelberg (2008)

    Book  Google Scholar 

  18. Nicolin, B., Gabler, R.: A knowledge-based system for the analysis of aerial images. IEEE Transactions on Geoscience and Remote Sensing GE-25(3), 317–329 (1987)

    Article  Google Scholar 

  19. Hay, G.J., Blaschke, T., Marceau, D.J., Bouchard, A.: A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing 57(5-6), 327–345 (2003)

    Article  Google Scholar 

  20. Aksoy, S., Koperski, K., Tusk, C., Marchisio, G., Tilton, J.: Learning bayesian classifiers for scene classification with a visual grammar. IEEE Transactions on Geoscience and Remote Sensing 43(3), 581–589 (2005)

    Article  Google Scholar 

  21. Serra, J.: Image Analysis and Mathematical Morphology. Theoretical Advances, vol. 2. Academic Press, New York (1988)

    Google Scholar 

  22. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1983)

    Google Scholar 

  23. Soille, P.: Morphological Image Analysis, Principles and Applications, 2nd edn. Springer, Berlin (2003)

    MATH  Google Scholar 

  24. Najman, L., Talbot, H.: Mathematical Morphology. Wiley-ISTE (August 2010)

    Google Scholar 

  25. Soille, P., Pesaresi, M.: Advances in mathematical morphology applied to geosciences and remote sensing. IEEE Transactions on Geoscience and Remote Sensing 40, 2042–2055 (2002)

    Article  Google Scholar 

  26. Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing 4(8), 1153–1160 (1995)

    Article  Google Scholar 

  27. Salembier, P.: Connected operators based on region-trees. In: Proc. 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 2176–2179 (2008)

    Google Scholar 

  28. Plaza, A., Benediktsson, J., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Tilton, J., Trianni, G.: Advanced processing of hyperspectral images. Remote Sensing of Environment 113(1), S110–S122 (2009)

    Google Scholar 

  29. Gualtieri, J.A., Tilton, J.: Hierarchical segmentation of hyperspectral data. In: AVIRIS Earth Science and Applications Workshop Proceedings, pp. 5–8 (2002)

    Google Scholar 

  30. Plaza, A., Tilton, J.: Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images. In: Proc.of IGARSS 2005 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  32. Valero, S., Salembier, P., Chanussot, J.: New hyperspectral data representation using binary partition tree. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 80–83 (2010)

    Google Scholar 

  33. Valero, S., Salembier, P., Chanussot, J.: Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data. In: 17th IEEE International Conference on Image Processing (ICIP 2010), pp. 2565–2568 (2010)

    Google Scholar 

  34. Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image segmentation using binary partition trees. Submitted to ICIP 2011, Brussels, Belgium (2011)

    Google Scholar 

  35. Valero, S., Salembier, P., Chanussot, J., Cuadras, C.: New binary partition tree construction for hyperspectral images: Application to object detection. In: Proc.of IGARSS 2011, Vancouver, Canada (2011)

    Google Scholar 

  36. Binaghi, E., Gallo, I., Pepe, M.: A cognitive pyramid for contextual classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 41(12), 2906–2922 (2004)

    Article  Google Scholar 

  37. Valero, S., Chanussot, J., Benediktsson, J., Talbot, H., Waske, B.: Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images. Pattern Recognition Letters 31(10), 1120–1127 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L.: Morphological attribute filters for the analysis of very high resolution remote sensing images. In: Proc. IEEE International Geoscience and Remote Sensing Symposium 2009, IGARSS 2009, vol. 3, pp. III–97–III–100 (July 2009)

    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 Transactions on Geoscience and Remote Sensing 48(10), 3747–3762 (2010)

    Article  Google Scholar 

  41. Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing 7(4), 555–570 (1998)

    Article  Google Scholar 

  42. Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Transactions on Image Processing 9(5), 860–872 (2000)

    Article  Google Scholar 

  43. Maragos, P., Ziff, R.: Threshold superposition in morphological image analysis systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 498–504 (1990)

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Alonso-Gonzalez, A., Lopez-Martinez, C., Salembier, P.: Filtering and segmentation of polarimetric SAR images with binary partition trees. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), pp. 4043–4046 (2010)

    Google Scholar 

  46. Dalla Mura, M., Benediktsson, B., Bruzzone, L.: Self-dual attribute profiles for the analysis of remote sensing images. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 306–319. Springer, Heidelberg (2011)

    Google Scholar 

  47. Wilkinson, M.H.F., Gao, H., Hesselink, W.H., Jonker, J.E., Meijster, A.: Concurrent computation of attribute filters on shared memory parallel machines. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1800–1813 (2008)

    Article  Google Scholar 

  48. Dalla Mura, M., Benediktsson, J., 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. Springer, Heidelberg (2011)

    Google Scholar 

  49. Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 43(3), 480–491 (2005)

    Article  Google Scholar 

  50. Falco, N., Dalla Mura, M., Bovolo, F., Benediktsson, J.A., Bruzzone, L.: Study on the capabilities of morphological attribute profiles in change detection on VHR images. In: Bruzzone, L. (ed.) Image and Signal Processing for Remote Sensing XVI. Proceedings of SPIE, vol. 7830. SPIE, Bellingham (2010)

    Chapter  Google Scholar 

  51. Alonso-Gonzalez, A., Lopez-Martinez, C., Salembier, P.: Filtering and segmentation of polarimetric sar images with binary partition trees. In: Proc. IEEE International Geoscience and Remote Sensing Symposium 2010, IGARSS 2010, Honolulu, USA, pp. 4043–4046 (2010)

    Google Scholar 

  52. Calderero, F., Marques, F.: Region merging techniques using information theory statistical measures. IEEE Trans. Image Processing 19, 1567–1586 (2010)

    Article  MathSciNet  Google Scholar 

  53. Hu, M.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  55. Cardoso, J., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Trans. Image Processing 14, 1773–1782 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland

    Jon Atli Benediktsson & Mauro Dalla Mura

  2. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

    Lorenzo Bruzzone & Mauro Dalla Mura

  3. GIPSA-Lab, Grenoble Institute of Technology, France

    Jocelyn Chanussot & Silvia Valero

  4. Technical University of Catalonia (UPC), Barcelona, Catalonia, Spain

    Philippe Salembier & Silvia Valero

Authors
  1. Jon Atli Benediktsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenzo Bruzzone
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jocelyn Chanussot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mauro Dalla Mura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Philippe Salembier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Silvia Valero
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute for the Protection and Security of the Citizen, European Commission, Joint Research Centre, via Enrico Fermi, 2749, 21027, Ispra (VA), Italy

    Pierre Soille , Martino Pesaresi  & Georgios K. Ouzounis ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Benediktsson, J.A., Bruzzone, L., Chanussot, J., Dalla Mura, M., Salembier, P., Valero, S. (2011). Hierarchical Analysis of Remote Sensing Data: Morphological Attribute Profiles and Binary Partition Trees. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds) Mathematical Morphology and Its Applications to Image and Signal Processing. ISMM 2011. Lecture Notes in Computer Science, vol 6671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21569-8_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21569-8_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21568-1

  • Online ISBN: 978-3-642-21569-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation