侧扫声纳图像特征提取与目标分类

J. Rhinelander
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引用次数: 14

摘要

侧扫声纳技术在过去的三十年中一直用于水下测量和成像。侧扫声纳的应用领域包括考古、安全防御、海底分类、环境调查等。近年来,自主水下系统的使用使自动收集数据成为可能。随着数据的自动收集,需要自动检测哪些信息是重要的。自动目标识别可以为安全和防御应用提供有效的任务规划和自主系统部署。支持向量机(svm)是一种经过验证的通用模式分类方法。它们提供的最大边际分类不会过度适合训练数据。一般认为,核函数的选择允许在分类系统中利用特定领域的信息。本文的研究表明,对于侧扫声纳的目标分类,额外的特征提取和数据工程比单独的参数优化可以获得更好的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature extraction and target classification of side-scan sonar images
Side-scan sonar technology has been used over the last three decades for underwater surveying and imaging. Application areas of side-scan sonar include archaeology, security and defence, seabed classification, and environmental surveying. In recent years the use of autonomous underwater systems has allowed for automatic collection of data. Along with automatic collection of data comes the need to automatically detect what information is important. Automatic target recognition can allow for efficient task planning and autonomous system deployment for security and defence applications. Support Vector Machines (SVMs) are proven general purpose methods for pattern classification. They provide maximum margin classification that does not over fit to training data. It is generally accepted that the choice of kernel function allows for domain specific information to be leveraged in the classification system. In this paper it is shown that for target classification in side-scan sonar, extra feature extraction and data engineering can result in better classification performance compared to parameter optimization alone.
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