Adaptive Neuro-Fuzzy Inference System for Echoes Classification in Radar Images

Leila Sadouki, Boualem Haddad

2016

Abstract

In order to remove the undesirable clutter which reduces the radar performances and causes significant errors in the rainfall estimation, we implemented in this paper an algorithm deals with the classification of radar echoes. The radar images studied are those recorded in Sétif (Algeria) every 15 minutes, we used a combination of textural approach, with the grey-level co-occurrence matrices, and a grid partition based fuzzy inference system, named ANFIS-GRID. We have used two parameters, namely Energy and local homogeneity that are considered to be the most effective in discriminating between precipitation echoes and clutter. Those parameters are used as inputs for the ANFIS-GRID, while the output of this system is the radar echo types. In function of the best mean rate of correct recognition and using two different optimization methods, the structure with 2 inputs, 4 membership functions, 16 rules and 1 output was selected as the most efficient ANFIS-GRID. This method gives a mean rate of correct recognition of echoes to over 93.52% (91.30% for precipitation echoes and 95.60% for clutter). In addition, the proposed approach gives a process maximum time of less than 90 seconds, which allows the filtering of the images in real time.

References

  1. Berenguer, M., Sempere-Torres, D., Corral, C. and Sanchez-Diezma, R., 2006. A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. Journal of Atmospheric and Oceanic Technology, vol. 23, pp. 1157-1180.
  2. Bhavani Sankar, A., Kumar, D., and Seethalakshmi, K., 2012. A New Self-Adaptive Neuro Fuzzy Inference System for the Removal of Non-Linear Artifacts from the Respiratory Signal. Journal of Computer Science. vol. 8 (5), 621-631.
  3. Chandrasekar, V., Keränen, R., Lim, S., and Moisseev, D., 2013. Recent advances in classification of observations from dual polarization weather radars. Atmospheric Research, vol. 119, pp. 97-111.
  4. Chaudhari, O. K., Khot, P. G., Deshmukh, K. C., and Bawne, N. G., 2012. ANFIS based model in decision making to optimize the profit in farm cultivation. International Journal of Engineering Science and Technology (IJEST). Vol. 4 (2), 442-448.
  5. Cho, Y. H., Lee, G., Kim, K. E. and Zawadzki, I., 2006. Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes. Journal of Atmospheric and Oceanic Technology. vol. 23, pp. 1206-1222.
  6. Doviak, R. J., and Zrnic, D. S., 1993. Doppler radar and weather observations, Academic Press., pp. 562.
  7. Haddad, B., Sadouki, L., Naili, R, Adane, A., and Sauvageot, H., 2003. Analyse De La Dimension Fractale Des Echos De Precipitations: Cas Des Inondations D'Alger. Publication de l 'Association Internationale de Climatologie. vol. 15, pp.386-392.
  8. Haddad, B., Adane, A., Sauvageot, H., and Sadouki, L., 2004. Identification and filtering of rainfall and ground radar echoes using textural features. International Journal of Remote Sensing. vol. 25(21), pp. 4641- 4656.
  9. Hamuzu, K., and Wakabayashi, M., 1991. Ground clutter rejection. In Hydological applications of Weather Radar, Clukie and Collier. Ed Ellis Horwood Ltd, pp. 131-142.
  10. Haralick, R. M., 1979. Statistical and structural approaches to textures. Proceedings of the IEEE on Image Processes, vol. 67, pp. 786-804.
  11. Hubbert, J. C., Dixon, M., and Ellis, S. M., 2009. Weather Radar Clutter. Part II: Real-Time identification and filtering. Journal of Atmospheric and Oceanic Technology, vol. 26, pp. 1181-1197.
  12. Islam, T., Rico-Ramirez, M. A., Han, D. and Srivastava, PK., 2012. Artificial Intelligence Techniques for Clutter Identification with Polarimetric Radar Signatures. Atmospheric Research, 109-110, pp. 95- 113.
  13. Kurian, C. P., George, V. I., Jayadev, B., and Radhakrishna, S. A., 2006. ANFIS Model For The Time Series Prediction of Interior Daylight illuminance. AIML Journal. Vol. 6 (3).
  14. Peckinpaugh, S. H., 1991. An Improved Method for Computing Grey-Level Co-Occurrence Matrix Based Texture Measures. CVGIP: Graphical Models and Image Processing. vol. 53, 574-580.
  15. Sadouki, L., and Haddad, B., 2013. Classification of radar echoes with a textural-fuzzy approach: an application for the removal of ground clutter observed in Sétif (Algeria) and Bordeaux (France) sites. Int. J. of Remote Sensing, vol. 34(21), 7447-7463.
  16. Sauvageot, H., and Despaux, G., 1990. SANAGA: Un système d'acquisition numérique et de visualisation des données radar pour la validité des estimations satellitaires de précipitations. Veille Climatique Satellitaire, vol. 31, pp. 51-55.
  17. Sauvageot, H., 1992. Radar Meteorology. Norwood: Artech House., pp. 361.
  18. Unser, M., 1986. Sum and difference histograms for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 8(1), pp. 118- 125.
  19. Wei, M., Bai, B., Sung, A. H., Liu, Q., Wang, J., and Cather, M. E., 2007. Predicting injection profiles using ANFIS. Information Sciences. vol. 177, 4445-4461.
  20. Xiang, L., 2010. Adaptive Network Fuzzy Inference System Used in Interference Cancellation of Radar Seeker. IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS). vol. 2, pp. 93-97.
Download


Paper Citation


in Harvard Style

Sadouki L. and Haddad B. (2016). Adaptive Neuro-Fuzzy Inference System for Echoes Classification in Radar Images . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 159-166. DOI: 10.5220/0005717401590166


in Bibtex Style

@conference{visapp16,
author={Leila Sadouki and Boualem Haddad},
title={Adaptive Neuro-Fuzzy Inference System for Echoes Classification in Radar Images},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005717401590166},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Adaptive Neuro-Fuzzy Inference System for Echoes Classification in Radar Images
SN - 978-989-758-175-5
AU - Sadouki L.
AU - Haddad B.
PY - 2016
SP - 159
EP - 166
DO - 10.5220/0005717401590166