基于多特征融合的声纳图像人造目标检测算法

Detection of Man-Made Object in Sonar Image based on Multi-Features Fusion

声纳图像中人造目标的自动检测是当前水下探测领域需要重点解决的问题之一。传统的基于目标回波信号强度的检测方法在海底存在岩石等类似于水雷等人造目标的情况下,常会导致较高的虚警率。由于人造目标和自然背景之问的纹理特性的不同,自然背景一般具有较复杂的纹理,而人造目标形状规则、表面光滑、纹理简单。为此,对基于扩展分形特征的声纳图像人造目标检测算法进行研究,同时利用灰度统计信息进行信息融合,给出了多特征融合的检测算法。仿真实验表明该方法在声呐图像人造目标检测中具有较好的检测性能。

the detection of man-made object in sonar image is one of crucial problems in the field of underwater detection. Conventional methods based on the intensity of target echo signal will result in high false alarming rate if mine-like object such as rock laying on the seafloor. For the texture difference between man-made object and natural background, natural background has always complicated texture and man-made object has simple texture and regular shape. Therefore, a novel detection algorithm based on multi-features fusion was studied, which integrated extended fractal （ EF ） and grey level statistic. These results indicate that the detection performance of this algorithm is satisfied.