# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 1 Introduction  
The classification of marine vessels is an important issue in maritime safety and traffic control. It has a broad application in both civil and military industries [1]. Compared with other target recognition problems, the classification of marine vessels is more difficult because of the large changes in viewing perspectives, illumination conditions, and scale, and the image background is disorganized [2]. According to the image types of marine vessels, there are mainly synthetic aperture radar images (SAR), spaceborne optical images (SOI), visible images and infrared images (IR). Because SAR images are characterized by all-day and all-weather imaging, [PERSON] et al. [3] developed an automatic ship wake detection system for spaceborne SAR images in 1996. However, the number of SAR sensors is limited, the revisit period is long, and the resolution is low. In 2010, [PERSON] et al. [4] conducted experiments on SOI image sets with higher resolution captured by optical sensors from multiple satellites, which can effectively distinguish between ships and non-ships, and obtain satisfactory ship detection performance. Similarly, satellite resources were still limited, and it is obviously more convenient for the camera to collect images. In 2015, [PERSON] et al. [5] published the world's first marine vessel dataset with paired visible and infrared images, which lead to progress in the research field of marine vessel classification. The visible image has sufficient detail and color information, and theinfrared image has a strong adaptability to the environment, which meant combining the two images yielded a higher accuracy of vessel classification. From the perspective of feature extraction, there are two types of methods for classifying marine vessels: methods based on traditional features and methods based on a convolutional neural network (CNN). Methods based on traditional features rely on artificially designed feature vectors for target recognition and classification [6; 7]. [PERSON] et al. [8] used the Histogram of Oriented Gradient (HOG) feature combined with the support vector machine (SVM) method to identify and classify the infrared ship targets on the sea surface, which can overcome the background interference to a certain extent. However, in the experiment, there are no tests on multiple targets, deformation, illumination, or other changes. [PERSON] et al. [9] used SIFT descriptors to identify ship targets in the port, and realized the invariance of illumination and angle by describing and matching the local features of the target. Yet using sliding window to extract features of targets resulted in high dimension leads to low computational efficiency. [PERSON] et al. [10] combined Fisher vector with Gaussian mixture model to linearly classify large-scale datasets. The target category contained more than one thousand kinds and the best classification accuracy was obtained by optimizing the loss function. The literature [11] has proposed that different features have their own advantages and disadvantages in different aspects, so the idea of using three features was synthetically adopted. The three features consist of multi-scale completed local binary patterns (MS-CLBP), Bag of visual words (BOVW), and spatial pyramid matching (SPM). Methods based on CNN mainly refer to select the features of a certain layer in the convolutional neural network, and then use the support vector machine (SVM), extreme learning machine (ELM) or logistic regression classifier [12; 13; 14; 15; 16; 17; 18]. The literature [5] has used the 15 th layer feature extracted from the pre-trained model vgg-16 based on the ImageNet dataset. Literature [12] has adopted AlexNet, VGG-16 and Inception-V3 to extract the features of the image, and then normalizes the different features into the same feature space. Literature [13] has designed a convolutional neural network extraction feature, and combined the traditional Gabor feature with MS-CLBP feature to describe the ship's target.