Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

The fine-grained ship image recognition task aims to identify various classes of ships.However,small inter-class,large intra-class differences between ships,and lacking of training samples are the reasons that make the task difficult.Therefore,to enhance the accuracy of the fine-grained ship image recognition,we design a fine-grained ship image recognition network based on bilinear convolutional neural network(BCNN)with Inception and additive margin Softmax(AM-Softmax).This network improves the BCNN in two aspects.Firstly,by introducing Inception branches to the BCNN network,it is helpful to enhance the ability of extracting comprehensive features from ships.Secondly,by adding margin values to the decision boundary,the AM-Softmax function can better extend the inter-class differences and reduce the intra-class differences.In addition,as there are few publicly available datasets for fine-grained ship image recognition,we construct a Ship-43 dataset containing 47,300 ship images belonging to 43 categories.Experimental results on the constructed Ship-43 dataset demonstrate that our method can effectively improve the accuracy of ship image recognition,which is 4.08%higher than the BCNN model.Moreover,comparison results on the other three public fine-grained datasets(Cub,Cars,and Aircraft)further validate the effectiveness of the proposed method.