An adaptive method using genetic fuzzy system to evaluate suspended particulates matters SPM from Landsat and Modis data

Authors:
Bahia Lounis;Sofiane Rabia;Adlene Ramoul;Aichouche Belhadj Aissa
Affiliations:
Laboratory of image processing and radiation, Faculty of Electronics and Computer Science, USTHB University, Algiers, Algeria;Laboratory of image processing and radiation, Faculty of Electronics and Computer Science, USTHB University, Algiers, Algeria;Laboratory of image processing and radiation, Faculty of Electronics and Computer Science, USTHB University, Algiers, Algeria;Laboratory of image processing and radiation, Faculty of Electronics and Computer Science, USTHB University, Algiers, Algeria
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

Citing 2
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Approaching the ocean color problem using fuzzy rules

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an optimization of fuzzy model which exploits remotely sensed multispectral reflectances to estimate Suspended Particulates Matters SPM concentrations in coastal waters. The relation between the SPM concentrations and the subsurface reflectances is modeled by a set of fuzzy rules extracted automatically from the data through two steps procedure. First, fuzzy rules are generated by unsupervised fuzzy clustering of the input data. In the second step, a genetic algorithm is applied to optimize the rules. Our contribution has focused on global and partial optimization of rules and a proposed chromosome structure adapted to remote sensing data. Results of the application of each type of optimization to Landsat and Modis data are shown and discussed.