基于超椭圆拟合的水下小目标分类*

水下小目标分类技术在海底探测、水下考古等方面应用广泛，在实际的水下声图像中，小目标投影产生的阴影区域通常在形状和尺寸方面显著于目标本身产生的亮区，故阴影分析算法对于目标的检测、识别和分类均有重要的研究意义。本文采用超椭圆曲线拟合算法拟合目标阴影区域，通过控制超椭圆函数的几个参数变化，实现不同的超椭圆曲线拟合不同的目标阴影形状，并将控制超椭圆曲线尺寸、形状和位置的参数作为特征向量输入到分类器，通过对比多个分类器得出分类结果，证明了以拟合参数为特征的分类方法有效。

Underwater small target classification based on superellipse fitting technology

Underwater small target classification technology is widely used in seabed detection, archaeology and so on. In the actual underwater acoustic image, the shape and size of shadow area, which produced by underwater small target, are usually more significant than bright area generated by the target itself, so shadow analysis algorithms have important research value in detection, identification and classification of target. In this paper, the superellipse fitting algorithm is used to fit the shadow area of the target. By controlling several parameters of superellipse function, different superellipse curves can be used to fit different target shadow shape. The parameters controlling the size, shape and position of superellipse curve are input into the classifier as feature vectors. By comparing the classification results of several classifiers, it is proved that the classification method characterized by fitting parameters is effective.