论文标题
通过视觉特征分类的时间对象光谱中的视觉特征在海上混乱中进行小浮动目标检测
Small-floating Target Detection in Sea Clutter via Visual Feature Classifying in the Time-Doppler Spectra
论文作者
论文摘要
检测海面障碍中的小浮动物体的表面雷达是一项挑战。在本文中，我们观察到，来自目标的反向散射在时间多普勒光谱（TDS）图像中刹车的连续性。遵循此视觉线索，我们利用本地二进制图案（LBP）来测量TDS图像中纹理的变化。结果表明，在LBP的特征空间中，雷达返回包含目标的雷达返回。然后使用无监督的一级支撑矢量机（SVM）来检测混乱的LBP直方图的偏差。检测器的外部归类为目标。在现实生活中的IPIX雷达数据集中，与其他三种现有方法相比，我们基于视觉功能的检测器显示出有利的检测率。
It is challenging to detect small-floating object in the sea clutter for a surface radar. In this paper, we have observed that the backscatters from the target brake the continuity of the underlying motion of the sea surface in the time-Doppler spectra (TDS) images. Following this visual clue, we exploit the local binary pattern (LBP) to measure the variations of texture in the TDS images. It is shown that the radar returns containing target and those only having clutter are separable in the feature space of LBP. An unsupervised one-class support vector machine (SVM) is then utilized to detect the deviation of the LBP histogram of the clutter. The outiler of the detector is classified as the target. In the real-life IPIX radar data sets, our visual feature based detector shows favorable detection rate compared to other three existing approaches.
