基于形状特征的水声图像小目标识别方法

为了抑制背景噪声,提高目标识别准确率,该文提出一种基于形状特征的水声图像小目标识别方法。对含有目标的水声图像进行非局部均值去噪处理后,使用OTSU算法自适应选取阈值对去噪图像进行二值化分割,结合形态学处理获得分割后的目标区域;提取目标区域的矩形度、圆形度、几何不变矩等各项形状参数,将目标的特征向量输入随机森林分类器实现对目标形状的识别。在仿真和实测数据集上分别进行了实验,结果表明,该方法对水声图像中的目标具有较高的识别率,可以实现不同高斯噪声背景下的目标识别,相较于其他方法在识别率上有一定提高。

Underwater sonar image small target recognition method based on shape features

In order to suppress background noise and improve the accuracy of target recognition, an underwater sonar image small target recognition method based on shape features is proposed in this paper. After performing non-local mean denoising on the underwater sonar image containing the target, the OTSU algorithm and morphological processing are used to adaptively select the threshold to perform binarization segmentation of the denoised image and obtain the target area; extract the rectangularity, circularity, geometric invariant moments and other shape parameters of the target area, input the feature vector into the random forest classifier to recognize the shape of target. Experiments have been carried out on simulation and measured data sets. The results show that this method has high recognition rate for targets in underwater sonar images, and can achieve target recognition under different Gaussian noise backgrounds. There is a certain improvement compared with other methods on recognition rate.