Authors:
Leila Sadouki
1
and
Boualem Haddad
2
Affiliations:
1
University M'Hamed Bougara of Boumerdes (UMBB) and Faculty of Computer Science and Electronics (USTHB), Algeria
;
2
Faculty of Computer Science and Electronics (USTHB), Algeria
Keyword(s):
ANFIS, Image, Radar, Precipitations, Clutter.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Shape Representation and Matching
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.
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