Comparison of Computational Intelligence Based Classification Techniques for Remotely Sensed Optical Image Classification

Several computational intelligence components, namely neural networks, fuzzy sets and genetic algorithms, have been applied separately or in combination to the process of remotely sensed data classification. By applying computational intelligence, we expect increased accuracy through the use of neural networks, optimal neural network structure and parameter determination via genetic algorithms, and transparency using fuzzy sets. This paper systematically reviews and compares several configurations in the particular context of remote sensing for land cover. In addition, some of the configurations used here, such as NEFCASS and CANFIS, have few previous applications in the field. Comparison of the configurations is achieved by testing the different methods with exactly the same case study data. Thorough assessment of results is performed by constructing an accuracy matrix for each training and testing data set. The evaluation of different methods is not only based on accuracy but also on compactness, completeness and consistency. The architecture, produced rule set, and training parameters for the specific classification task, are presented. Some comments and directions for future work are given.

STATHAKIS Dimitrios;  VASILAKOS A.; 

2007-04-13

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

JRC36834

https://publications.jrc.ec.europa.eu/repository/handle/JRC36834,   

10.1109/TGRS.2006.872903,