Skip to main content
SpringerLink
Log in
Book cover

Mexican International Conference on Artificial Intelligence

MICAI 2015: Advances in Artificial Intelligence and Its Applications pp 382–392Cite as

Classification of Different Vegetation Types Combining Two Information Sources Through a Probabilistic Segmentation Approach

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9414))

Abstract

In this work we propose a new probabilistic segmentation model that allows us to combine more than one likelihood. The algorithm is applied to identify vegetation types in images from Landsat 5 satellite. Firstly, we obtain histograms from two information sources: spectral bands and principal components obtained from vegetation indices. Then, given an image, we compute two likelihoods of pixels to belong to each class (vegetation type), one for each source of information. The computed likelihoods are the inputs of the proposed probabilistic segmentation algorithm. This algorithm gives an estimation of the probability of a pixel of belonging to a class. The final segmentation is easily obtained by maximizing the estimated discrete probability for each pixel of the image. Experiments with real data show that the proposed algorithm obtains competitive results compared with state of the art algorithms.

This is a preview of subscription content, 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 EPUB and 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

Learn about institutional subscriptions

Notes

  1. 1.

    The experts work at Land Information Institute of Jalisco (IITEJ).

References

  1. Barnes, E.M., Clarke, T.R.: Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Proceedings of the Fifth International Conference on Precision Agriculture (2000)

    Google Scholar 

  2. Broge, N.H., Leblanc, E.: Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76(2), 156–172 (2001)

    Article  Google Scholar 

  3. Chen, D., Huan, J., Jackson, T.J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from modis near- and short-wave infrared bands. Remote Sens. Environ. 98, 225–236 (2005)

    Article  Google Scholar 

  4. De Wit, A.J.W., Clevers, J.G.P.W.: Efficiency and accuracy of per-field classification for operational crop mapping. Int. J. Remote Sens. 25(20), 4091–4112 (2004)

    Article  Google Scholar 

  5. Gitelson, A.A., Merzlyak, M.N.: Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160(3), 271–282 (2003)

    Article  Google Scholar 

  6. Gitelson, A.A.: Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161(2), 165–173 (2004)

    Article  Google Scholar 

  7. Huete, A.R., Liu, H., Batchily, K., Van Leeuwen, W.: A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Elsevier 59, 440–451 (1997)

    Google Scholar 

  8. Jackson, R.D., Huete, A.R.: Interpreting vegetation indices. Remote Sens. Environ. 8(2), 185–200 (1979)

    Google Scholar 

  9. Ji, L., Zhang, L., Bruce, W.: Analysis of dynamic thresholds for the normalized difference water index. Photogram. Eng. Remote Sens. 75(11), 1307–1317 (2009)

    Article  Google Scholar 

  10. Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., Xiao, T.: An automated method for extracting rivers and lakes from landsat imagery. Remote Sens. 6(6), 5067–5089 (2014)

    Article  Google Scholar 

  11. Jordan, C.: Derivation of leaf area index from quality of light on the forest floor. Ecology 50(4), 663–666 (1969)

    Article  Google Scholar 

  12. Jordan, C.: A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25(3), 295–309 (1988)

    Article  Google Scholar 

  13. Karakahya, H., Yazgan, B., Ersoy, O.K.: A spectral-spatial classification algorithm for multispectral remote sensing data. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1011–1017. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Kaufman, Y., Tanre, D.: Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. Geosci. Remote Sens. 30(2), 261–270 (1992)

    Article  Google Scholar 

  15. Kettig, R.L., Landgrebe, D.A.: Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects. IEEE Trans. Geosci. Electron. 14(1), 19–26 (1976)

    Article  Google Scholar 

  16. Landgrebe, D.: The development of a spectral-spatial classifier for earth observational data. Pattern Recogn. 12(3), 165–175 (1980)

    Article  Google Scholar 

  17. Marroquín, J.L., Botello, S., Calderón, F., Vemuri, B.C.: The MPM-MAP algorithm for image segmentation. Pattern Recogn. 1, 303–308 (2000)

    Google Scholar 

  18. Marroquin, J.L., Velasco, F.A., Rivera, M., Nakamura, M.: Gauss-markov measure field models for low-level vision. IEEE Trans. Pattern Anal. Mach. Intell. 23(4), 337–348 (2001)

    Article  Google Scholar 

  19. Moore, D.M., Lees, B.G., Davey, S.M.: A new method for predicting vegetation distributions using decision tree analysis in a geographic information system. Environ. Manage. 15(1), 59–71 (1991)

    Article  Google Scholar 

  20. Northrop, A., Team, L.S.: Ideas-lansat products description document. Technical report, Telespazio VEGA UK Ltd. (2015)

    Google Scholar 

  21. Oliva, F.E., Dalmau, O.S., Alarcón, T.E.: A supervised segmentation algorithm for crop classification based on histograms using satellite images. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014, Part I. LNCS, vol. 8856, pp. 327–335. Springer, Heidelberg (2014)

    Google Scholar 

  22. Omkar, S.N., Senthilnath, J., Mudigere, D., Kumar, M.M.: Crop classification using biologically-inspired techniques with high resolution satellite image. J. Indian Soc. Remote Sens. 36(2), 175–182 (2008)

    Article  Google Scholar 

  23. Pena-Barragán, J., Ngugi, M., Plant, R., Six, J.: Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 115, 1301–1316 (2011)

    Article  Google Scholar 

  24. Pulido, H.G., Bautista, A.M., Guevara, R.M.: Jalisco territorio y problemas de desarrollo. iterritorial (2013)

    Google Scholar 

  25. Rokni, K., Ahmad, A., Selamat, A., Hazini, S.: Water feature extraction and change detection using multitemporal landsat imagery. Remote Sens. 6(5), 4173–4189 (2014)

    Article  Google Scholar 

  26. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C.: Monitoring the vernal advancements and retrogradation of natural vegetation. Technical report, NASA/GSFC (1974)

    Google Scholar 

  27. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  28. Su, B., Noguchi, N.: Agricultural land use information extraction in miyajimanuma wetland area based on remote sensing imagery. Environ. Control. Biol. 50(3), 277–287 (2012)

    Article  Google Scholar 

  29. Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2), 127–150 (1979)

    Article  Google Scholar 

  30. Ustuner, M., Sanli, F., Abdikan, S., Esetlili, M., Kurucu, Y.: Crop type classification using vegetation indices of rapideye imagery. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 195–198 (2014)

    Google Scholar 

  31. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2001)

    Google Scholar 

  32. Wang, H., Zhang, J., Xiang, K., Liu, Y.: Classification of remote sensing agricultural image by using artificial neural network. In: International Workshop on Intelligent Systems and Applications, pp. 1–4, May 2009

    Google Scholar 

  33. Weichelt, H., Rosso, P., Marx, A., Reigber, S., Douglass, K., Heynen, M.: The rapideye red edge band. Technical report, BlackBridge (2012)

    Google Scholar 

  34. Yashon, O., Tateishi, R.: A water index for rapid mapping of shoreline changes of five east african rift valley lakes: an empirical analysis using landsat TM and ETM+ data. Int. J. Remote Sens. 27(15), 3153–3181 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

We thank Maximiliano Bautista Andalón and Ana Teresa Ortega Minakata, members of Land Information Institute of Jalisco (IITEJ), for providing the ground truth images and the required information for this research.

Author information

Authors and Affiliations

  1. Centro Universitario de los Valles, Universidad de Guadalajara, Ameca, Jalisco, Mexico

    Francisco E. Oliva, Teresa E. Alarcón & Miguel De-La-Torre

  2. Centro de Investigación en Matemáticas, Guanajuato, Mexico

    Oscar S. Dalmau

Authors
  1. Francisco E. Oliva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar S. Dalmau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Teresa E. Alarcón
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miguel De-La-Torre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco E. Oliva .

Editor information

Editors and Affiliations

  1. Unidad Profesional Interdisciplinaria, México DF, Mexico

    Obdulia Pichardo Lagunas

  2. Universidad Autónoma Metropolitana, México DF, Mexico

    Oscar Herrera Alcántara

  3. Instituto de Investigaciones Eléctricas, Cuernavaca, Morelos, Mexico

    Gustavo Arroyo Figueroa

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Oliva, F.E., Dalmau, O.S., Alarcón, T.E., De-La-Torre, M. (2015). Classification of Different Vegetation Types Combining Two Information Sources Through a Probabilistic Segmentation Approach. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27101-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27100-2

  • Online ISBN: 978-3-319-27101-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation