SVM-Based Sea-Surface Small Target Detection: A False-Alarm-Rate-Controllable Approach
SVM-Based Sea-Surface Small Target Detection: A False-Alarm-Rate-Controllable Approach
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基于支持向量机的海面小目标检测：一种误报率可控的方法
DOI：
10.1109/lgrs.2019.2894385
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发表时间：
2019-08-01
影响因子：
4.8
通讯作者：
Qi, Peihan
中科院分区：
文献类型：
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作者：
Li, Yuzhou;Xie, Pengcheng;Qi, Peihan
在这封信中，我们考虑不同的检测环境，以解决在海杂波中检测小目标的问题。我们首先从时域和频域的返回信号中提取三个简单但实际上有区别的特征，然后将它们融合到一个3-D特征空间中。基于构造的空间，我们然后采用并优雅地修改支持向量机设计一个基于学习的检测器，包围虚警率（FAR）。最重要的是，我们提出的检测器可以灵活地控制FAR通过简单地调整两个引入的参数，这有利于调节检测器的灵敏度所引起的海尖峰和公平地评估不同的检测算法的性能。实验结果表明，我们提出的检测器显着提高检测概率比现有的几个经典检测器在低信号杂波比（高达58%）和低FAR（高达40%）的情况下。
In this letter, we consider the varying detection environments to address the problem of detecting small targets within sea clutter. We first extract three simple yet practically discriminative features from the returned signals in the time and frequency domains and then fuse them into a 3-D feature space. Based on the constructed space, we then adopt and elegantly modify the support vector machine to design a learning-based detector that enfolds the false alarm rate (FAR). Most importantly, our proposed detector can flexibly control the FAR by simply adjusting two introduced parameters, which facilitates to regulate detector's sensitivity to the outliers incurred by the sea spikes and to fairly evaluate the performance of different detection algorithms. Experimental results demonstrate that our proposed detector significantly improves the detection probability over several existing classical detectors in both low signal to clutter ratio (up to 58%) and low FAR (up to 40%) cases.