基于相似性的声纳ATR系统在线领域自适应数据采掘
Similarity-based data mining for online domain adaptation of a sonar ATR system
摘要：
由于现场数据采集的昂贵性，训练数据的缺乏往往限制了自动目标识别（ATR）系统的性能。通常采用领域自适应技术来解决这一问题，但现有的方法不能满足水下系统资源和时间的限制。我们建议通过使用一种新的数据选择方法在线微调ATR算法来解决这个问题。我们提出的数据挖掘方法依赖于视觉相似性，优于传统的硬挖掘方法。我们提出了一个比较性能分析，在广泛的模拟环境中，并强调使用我们的方法快速适应以前看不见的环境的好处。
英文摘要：
Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existing methods fail to satisfy the constraints of resource and time-limited underwater systems. We propose to address this issue via an online fine-tuning of the ATR algorithm using a novel data-selection method. Our proposed data-mining approach relies on visual similarity and outperforms the traditionally employed hard-mining methods. We present a comparative performance analysis in a wide range of simulated environments and highlight the benefits of using our method for the rapid adaptation to previously unseen environments.
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