Back to results list
Please use this identifier to cite or link to this item:
|Title:||An approach to seafloor classification using fuzzy neural networks combined with a genetic algorithm||Authors:||Zhou, Xinghua||Keywords:||Hong Kong Polytechnic University -- Dissertations
Ocean bottom -- Classification
Neural networks (Computer science)
|Issue Date:||2005||Publisher:||The Hong Kong Polytechnic University||Abstract:||The seafloor classification, either in terms of the physical properties or geological provinces, is important in many fields including the marine geology, hydrography, marine engineering, environmental sciences, and fisheries. The purpose of this research is to develop automated seafloor classification algorithms of the backscatter data from multibeam sonar. The algorithms include the processing of backscatter data, feature extraction and selection, and the classification approaches involved. The raw backscatter data from multibeam sonar must be analyzed and processed, because it is difficult to use directly in the classification process. This research focuses on the correction of local bottom slope algorithms and the influences near nadir reflection. Through these corrections and other compensations, we can obtain processed backscatter strength data which better reflects the features of the seafloor. These processes provide the data foundation for the later classification process. Feature extraction and feature selection are essential steps in order to optimize a pattern classification system. In this work, a feature subset selection method using a Genetic Algorithm (GA) combined with a fuzzy ARTMAP neural network (FAMNN) classifier is proposed. The complete feature set is encoded in a chromosome and then optimized by GA algorithms with respect to both classification accuracy and number of selected features. The experimental results show that the classification performance is improved or at least kept similar using the feature set of 4 or 6 features selected from all 24 features.
Neural network classifiers are nonparametric and thus more robust than traditional statistical classifiers that typically require knowledge of the underlying probability distributions. In this thesis, at first a simplified fuzzy ARTMAP neural network (FAMNN) is investigated for the seafloor classification. The performance effects of variations in choice parameter, vigilance parameter, baseline vigilance parameter, voting strategy and the size of the training set are examined with a real data set. The performance of the FAMNN classifier has been compared with the traditional Bayesian classifier as a statistical benchmark on the same database. The FAMNN classifier performs better than that of the traditional Bayesian classifier in terms of every seabed type and the accuracies of total classification. However, the classification performance of the FAMNN classifier is highly dependent on an adequate number of samples to train the classifier, a novel fuzzy ARTMAP neural network variant (GA-FAMNN) is proposed which employs a GA strategy to search and generate new input pattern samples to fall near the boundaries between categories. The FAMNN classifier undergoes supervised training again with the original existing training set and the new augmenting samples. The two experimental results illustrate that the performance of the retrained FAMNN classifier has evidently improved using the proposed method. This is particularly so when there are a relatively small number of ground-truth samples. These approaches have been proved to be useful and meaningful for the seabed classification.
|Description:||ix, 98 leaves : ill. (some col.) ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P LSGI 2005 Zhou
|URI:||http://hdl.handle.net/10397/3964||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
Show full item record
Files in This Item:
|b1896705x_link.htm||For PolyU Users||161 B||HTML||View/Open|
|b1896705x_ir.pdf||For All Users (Non-printable)||2.68 MB||Adobe PDF||View/Open|
Citations as of Dec 10, 2018
Citations as of Dec 10, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.