Automatic interpretation of sonar image sequences using temporal feature measures
Automatic interpretation of sonar image sequences using temporal feature measures
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DOI：
10.1109/48.557539
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发表时间：
1997
影响因子：
4.1
通讯作者：
M. Chantler;J. Stoner
中科院分区：
文献类型：
--
作者：
M. Chantler;J. Stoner
本文报道了扇形扫描声纳数据自动判读系统的研制。它建议使用从声纳扫描序列得出的新的特征测量组合来表征目标随时间的返回行为。之前的研究使用的是来自单一声纳扫描的灰度和形状描述符。然而，随着时间的推移，目标的返回变化很大(例如潜水员、无人潜航器和船只的尾迹)，就会遇到问题。因此，通过将现有的一维时间度量和二维对象描述符相结合，开发了一组新的时间特征度量。这些新特征提供了对目标在声纳扫描序列中的二维回波行为的定量描述。使用有限但真实的数据集进行的实验表明，使用这些新特征可以显著提高分类精度。观察到使用“静态”特征测量(源自单次扫描)，当它们应用于数据集时，其分类误差在7%至10%之间。相比之下，使用时间测量将这一错误率降低到1%或2%，在某些情况下甚至降低到零。
This paper reports the development of a system for the automated interpretation of sector scan sonar data. It proposes the use of a new combination of feature measures derived from sequences of sonar scans to characterize the behaviour of targets' returns over time. Previous research used grey-scale and shape descriptors derived from single sonar scans. However, problems were experienced with targets whose return varied significantly over time (such as divers, UUV's, and ships' wakes). Hence a new set of temporal feature measures has been developed by combining existing one-dimensional temporal measures and two-dimensional object descriptors. These new features provide a quantitative description of the behaviour of a target's two-dimensional returns over a sequence of sonar scans. Experiments with a limited but real data set have shown that classification accuracy can be significantly improved by the use of these new features. The use of "static" feature measures (derived from a single scan) was observed to give classification errors of between 7% and 10% when they were applied to the data set. In contrast, the use of temporal measures reduced this error rate to 1% or 2% and in some cases reduced it to zero.