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.

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