Advertisement

Segmentation and Classification of Geoenvironmental Zones of Interest in Aerial Images Using the Bounded Irregular Pyramid

  • Mariletty Calderón
  • Rebeca Marfil
  • Antonio BanderaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9667)

Abstract

The goal of this work is to automatically detect and classify a set of geoenvironmental zones of interest in panchromatic aerial images. Focused on a specific area, the zones to be detected are vegetation/mangrove, degradation/desertification, interface water-sediment and plain. These zones are very interesting from a geological point of view due to their spatial distribution and interrelation, which contribute to evaluate the natural anthropic impact level. The approach to unsupervisedly extract these zones from an input image has two steps. Firstly, the image is automatically segmented in homogeneous colored regions using the Bounded Irregular Pyramid (BIP). The BIP is a hierarchy of successively reduced graphs which produces accurate segmentation results with a low computational cost. Secondly, each obtained region is classified using texture features to determine if it belongs to one of the geoenvironmental zones of interest. As texture features, we have evaluated two variations of the Local Binary Pattern (LBP) descriptor: the Extended-LBP (ELBP) and the LBP variance (LBPV). Both methods include a local contrast measure. For classifying the obtained features, the Support Vector Machine (SVM) has been employed. At this stage, we have evaluated the use of linear and radial basis function (RBF) kernels. The whole framework was tested using images obtained from our specific area of interest: the location of Carenero, Miranda state (Venezuela), in years 1936 and 1992. They allow to study the variation of the geoenvironmental zones of interest of this location in this period of time. These images are low quality images and present significant variations in illumination. This makes difficult the texture classification of their zones. However, the obtained results show that the proposed approach provides good results in terms of identification of zones of geoenviromental interest in these images.

Keywords

Aerial image segmentation Irregular pyramid Texture classification 

Notes

Acknowledgments

This paper has been partially supported by the Spanish Ministerio de Economía y Competitividad TIN2015-65686-C5 and FEDER funds.

References

  1. 1.
    Sulong, I., Mohd-Lokman, H., Mohd-Tarmizi, K., Ismail, A.: Mangrove mapping using landsat imagery and aerial photographs: Kemaman district, Terengganu, Malaysia. Environ. Dev. Sustain. 4(2), 135–152 (2002)CrossRefGoogle Scholar
  2. 2.
    Hirche, A., Salamani, M., Abdellaoui, A., Benhouhou, S., Valderrama, J.M.: Landscape changes of desertification in arid areas: the case of south-west Algeria. Environ. Monit. Assess. 179(1–4), 403–420 (2011)CrossRefGoogle Scholar
  3. 3.
    Risojevic, V., Momic, S., Babic, Z.: Gabor descriptors for aerial image classification. In: Adaptive and Natural Computing Algorithms, pp. 51–60 (2011)Google Scholar
  4. 4.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lingua, A., Marenchino, D., Nex, F.: Performance analysis of the SIFT operator for automatic feature extraction and matching in photogrammetric applications. Sensors 9(5), 3745–3766 (2009)CrossRefGoogle Scholar
  6. 6.
    Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP Variance (LBPV) with global matching. Pattern Recogn. 43, 706–719 (2010)CrossRefzbMATHGoogle Scholar
  7. 7.
    Marfil, R., Bandera, A.: Comparison of perceptual grouping Criteria within an integrated hierarchical framework. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 366–375. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 19(3), 51–59 (1996)CrossRefGoogle Scholar
  9. 9.
    Ojala, T., Pietikäinen, M.: Multirresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  10. 10.
    Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the ACM SIGSPATIAL GIS, pp. 270–279 (2010)Google Scholar
  11. 11.
    Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  12. 12.
    Antnez, E., Marfil, R., Bandera, J.P., Bandera, A.: Part-based object detection into a hierarchy of image segmentations combining color and topology. Pattern Recogn. Lett. 34(7), 744–753 (2013)CrossRefGoogle Scholar
  13. 13.
    Hengl, T., Toomanian, N., Reuter, H., Malakouti, M.: Methods to interpolate soil categorical variables from profile observations: Lessons from Iran. Geoderma 140, 417–427 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mariletty Calderón
    • 1
  • Rebeca Marfil
    • 1
  • Antonio Bandera
    • 1
    Email author
  1. 1.Departamento de Tecnología ElectrónicaUniversidad de MálagaMálagaSpain

Personalised recommendations