# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 1 Introduction
[12] proposed several image classification and feature extraction algorithms on ship imagery, which obtained good results. [PERSON] [13] developed the local binary patterns (LBP) operator for vessel classification. [PERSON] et al. [14] used the bag of visual words (BOVW) in vessel classification. These methods, however, mainly focus on extraction of low-level visual features. Image backgrounds in actual scenarios can be complex. Additionally, different illumination intensities and viewing angles affect feature extraction. The color and texture information corresponding to the same object may differ in different images, thereby resulting in inaccurate feature extraction, which in turn, leads to lower classification accuracy. Methods based on extraction of handcrafted features, therefore, are greatly limited in terms of their ability to express images accurately. Consequently, they are only suitable for use in specific applications and rely on expert knowledge. In recent years, deep learning methods that integrate feature extraction and classifier training to realize end-to-end machine learning have witnessed rapid development. The convolution operation automatically acquires structural information by combining low-level features to form high-level features that are more abstract. Since the emergence of convolutional neural networks at the ImageNet Challenge, deep learning methods have come to be recognized as a favorable means for solving target recognition problems, such as classification, positioning, and detection. In addition, such methods have been successfully employed in such applications as speech recognition [15], behavior detection [16], image classification [17], traffic-sign recognition [18], and other tasks. Since the labeling process of visible ship images is difficult and expensive, the number of ship images is often very small. Hence, at present, most research concerning ship classification is based on satellite and synthetic aperture radar (SAR) imaging. Only very few studies have been performed concerning class recognition of ships based on images captured by a camera. [PERSON] et al. [19] designed a convolutional neural network (CNN) to facilitate ship-class recognition based on satellite imaging, thereby demonstrating attainment of high classification efficiency. [PERSON] et al. [20] also proposed a CNN model capable of operating on multi-resolution input, and evaluated its classification performance by providing as input TerraSAR-X images comprising five maritime classes--cargo, tanker, windmill, platform, and harbor structure. Providing images containing a combination of different resolutions as input helps improve classification accuracy. However, further investigation needs to be performed to understand how changes in image resolution affects internal activations within CNN. [PERSON] et al. [21] proposed a CNN-based method involving extreme learning machine for recognition of infrared ship images. This method was applicable to infrared-based recognition systems, and required an extreme learning machine based ensemble for image classification post learning of CNN features. Consequently, the algorithm on which this method was based was rather complicated. With technological advancements, deep-classification models, such as AlexNet [22], very deep convolutional networks (VGGNet) [23], and ResNet [24] have also been developed. [PERSON] et al. [25] used deep CNN with multi-scale rotation invariant features to facilitate ship classification. The classification accuracy achieved equaled 98.33% when operating on the BCCT200-RESIZE (barges, cargo ships, container ships, and tankers, 200 images per class) dataset [26]; however, when operating on the visible and infrared spectrums (VAIS) dataset [27], the classification accuracy recorded equaled only 88%. [PERSON] et al. [28] proposed a method for ship detection and classification based on remote sensing images with an improved residual network. However, the dataset was small, thereby resulting in overfitting. [PERSON] et al.