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首页> 外文会议>Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII >Investigation of Training Sample Selection Methods for Object Classification in Sonar Imagery
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Investigation of Training Sample Selection Methods for Object Classification in Sonar Imagery

机译：声纳成像目标分类的训练样本选择方法研究

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摘要

For Sonar Automatic Target Recognition problems, the number of mine-like objects is relatively small compared to the number non-mine-like objects available. This creates a heavy bias towards non-mine-like objects and increases the processing resources needed for classifier training. In order to reduce resource needs and the bias towards non-mine-like objects, we investigate selection methods for reducing the non-mine-like target samples while still maintaining as much of the original training information as possible. Specifically, we investigate methods for reducing sample size and bias while maintaining good classifier performance. Several methods are considered during this investigation that cover a wide range of techniques, including clustering and evolutionary algorithms. Each method is evaluated based on the classifier performance when trained on the chosen data samples and the execution time to select the new training set. Results on each method tested are presented using sonar data collected using a sidescan sonar system.
机译：对于声纳自动目标识别问题，与可用的非地雷类对象相比，类地雷对象的数量相对较少。这对非类雷物体产生了严重偏差，并增加了分类器训练所需的处理资源。为了减少资源需求和对非地雷类目标的偏见，我们研究了选择方法，以减少非地雷类目标样本，同时仍保持尽可能多的原始训练信息。具体来说，我们研究了在保持良好的分类器性能的同时减少样本量和偏差的方法。在此研究过程中考虑了几种方法，这些方法涵盖了广泛的技术，包括聚类和进化算法。在选择的数据样本上训练时，基于分类器性能评估每种方法，并根据执行时间选择新的训练集。使用通过侧扫声纳系统收集的声纳数据显示每种测试方法的结果。

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