基于信息熵和残差神经网络的多层次船只目标鉴别方法

Multi-level Ship Target Discrimination Method Based on Entropy and Residual Neural Network

为剔除船只候选区域中的虚警目标,提出了一种基于信息熵和残差神经网络的多层次虚警鉴别方法。首先,基于船只和虚警图像切片在信息熵上的差异,采用信息熵阈值来去除候选区域中的大部分虚警。为进一步确认船只目标,设计了一种用于图像切片分类的深层残差神经网络模型,并采用网络“微调”的迁移学习策略对图像分类网络模型进行训练,实现对船只目标和虚警的自动分类。实验结果表明,该方法取得了不错的鉴别效果,能有效剔除岛屿、云层、海杂波等虚警,方法简单高效,后续无须进行复杂的鉴别工作。

In order to remove false alarms in the candidate regions of ship target,a multi-level false alarms discrimination method based on entropy and residual neural network is proposed.Firstly,based on the difference in entropy between the image slices of ships and false alarms,the most false alarms in the candidate regions are removed with the threshold of entropy.In order to confirm the ship target,a deep residual neural network model for image slice classification is designed and the transfer learning method called finetuning is adopted to train deep residual neural network,to realize the automatic classifying of the ship and false alarm.Experimental results show that the proposed method achieves a good discrimination effect and achieves effective elimination of false alarms such as islands,clouds and sea clutter.It is simple and efficient,and no complicated identification work is needed in the subsequent process.