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Manifold-Based Classification of Underwater Unexploded Ordnance in Low-Frequency Sonar
IEEE Journal of Oceanic Engineering ( IF 4.1 ) Pub Date : 2020-07-01 , DOI: 10.1109/joe.2019.2916942
Nick H. Klausner , Mahmood R. Azimi-Sadjadi

This paper addresses the problem of discriminating underwater unexploded ordnance (UXO) from non-UXO objects using manifold learning principles when applied to data collected from low-frequency sonar. Our classification hypothesis is that the sequence of measurements collected from an object lie in some low-dimensional subspace which is locally linear but globally nonlinear. These low-dimensional features and their behavior on the manifold can then be used to discriminate among various UXO and non-UXO objects that may be encountered in shallow water environments. With this goal in mind, techniques are developed to not only learn the manifold but also to provide an out-of-sample embedding for newly acquired data. The manifold features from the training set are then used to construct local linear subspaces for representing each newly embedded testing feature vector. A statistical-based technique is then used to select the most likely class label by finding the class which best represents the data. The ability of the classifier to discriminate among multiple object types is then demonstrated using a sonar data set collected from underwater objects in a controlled setting. Classification results are presented and compared with an alternative method that also relies on a set of features extracted using manifold learning principles.

中文翻译:

基于流形的低频声纳水下未爆弹药分类

本文解决了在应用于从低频声纳收集的数据时使用流形学习原理区分水下未爆弹药 (UXO) 和非 UXO 物体的问题。我们的分类假设是从对象收集的测量序列位于某些局部线性但全局非线性的低维子空间中。然后,这些低维特征及其在流形上的行为可用于区分浅水环境中可能遇到的各种未爆炸弹药和非未爆炸弹药。考虑到这一目标,开发的技术不仅可以学习流形,还可以为新获取的数据提供样本外嵌入。然后使用训练集中的流形特征来构建局部线性子空间,以表示每个新嵌入的测试特征向量。然后使用基于统计的技术通过找到最能代表数据的类来选择最可能的类标签。然后使用从受控设置中的水下物体收集的声纳数据集来证明分类器区分多种物体类型的能力。呈现分类结果并与另一种方法进行比较,该方法也依赖于使用流形学习原理提取的一组特征。然后使用从受控设置中的水下物体收集的声纳数据集来证明分类器区分多种物体类型的能力。呈现分类结果并与另一种方法进行比较,该方法也依赖于使用流形学习原理提取的一组特征。然后使用从受控设置中的水下物体收集的声纳数据集来证明分类器区分多种物体类型的能力。呈现分类结果并与另一种方法进行比较,该方法也依赖于使用流形学习原理提取的一组特征。
更新日期:2020-07-01
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