Abstract: 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 methodcalled 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.

Key words: Entropy, False alarm discrimination, Residual neural network, Transfer learning