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An approach to seafloor classification using fuzzy neural networks combined with a genetic algorithm.

机译：一种使用模糊神经网络结合遗传算法进行海底分类的方法。

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摘要

Seafloor classification, either in terms of physical properties or geological provinces, is important in many fields including 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 algorithms of local bottom slope and near nadir reflection influences. 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 outperforms the traditional Bayesian classifier in terms of every seabed type and total classification accuracies. 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.
机译：无论是在物理性质还是在地质上，海底分类在许多领域都很重要，包括海洋地质，水文学，海洋工程，环境科学和渔业。这项研究的目的是为多波束声纳的反向散射数据开发自动海底分类算法。这些算法包括反向散射数据的处理，特征提取和选择以及所涉及的分类方法。必须对来自多束声纳的原始反向散射数据进行分析和处理，因为很难直接在分类过程中使用。本文的研究重点是局部底坡和近天底反射影响的校正算法。通过这些更正和其他补偿，我们可以获得处理后的散射强度数据，该数据可以更好地反映海底特征。这些过程为以后的分类过程提供了数据基础。特征提取和特征选择是优化模式分类系统的必要步骤。在这项工作中，提出了一种结合遗传算法（GA）和模糊ARTMAP神经网络（FAMNN）分类器的特征子集选择方法。完整的特征集在染色体中编码，然后通过GA算法针对分类准确性和所选特征的数量进行优化。实验结果表明，使用从全部24个特征中选择的4个或6个特征的特征集，可以提高分类性能或至少保持相似。神经网络分类器是非参数的，因此比通常需要了解基本概率分布的传统统计分类器更可靠。本文首先研究了简化的模糊ARTMAP神经网络（FAMNN）用于海底分类。使用真实数据集检查选择参数，警戒参数，基线警戒参数，投票策略和训练集大小的变化对性能的影响。 FAMNN分类器的性能已与传统贝叶斯分类器进行了比较，作为同一数据库的统计基准。在每种海床类型和总分类精度方面，FAMNN分类器均优于传统贝叶斯分类器。但是，FAMNN分类器的分类性能高度依赖于训练分类器的样本数量。提出了一种新颖的模糊ARTMAP神经网络变量（GA-FAMNN），该变量采用GA策略来搜索和生成新的输入模式样本，使其落在类别之间的边界附近。 FAMNN分类器使用原始的现有训练集和新的扩充样本再次进行监督训练。这两个实验结果表明，使用所提出的方法，经过重新训练的FAMNN分类器的性能已得到明显改善。当实地样本的数量相对较少时，尤其如此。

著录项

  • 作者

    Zhou, Xinghua.;

  • 作者单位

    Hong Kong Polytechnic University (People's Republic of China).;

  • 授予单位 Hong Kong Polytechnic University (People's Republic of China).;
  • 学科 Geology.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 98 p.
  • 总页数 98
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地质学;
  • 关键词

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