# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 1 Introduction  
In accordance with global economic expansion and increasing foreign trade, maritime traffic has witnessed tremendous growth, and this has resulted in an obvious increase in the number of ships taking to the oceans. Ship classification assumes great importance with regard to several aspects, such as maritime safety, traffic monitoring, and maritime-domain awareness [1; 2]. In addition, with the recent advancement in artificial intelligence, the concept of intelligent ships [3] is being projected as the next big thing, as regards to the future of the maritime industry. Intelligent ships are expected to be characterized by safety, reliability, energy-saving potential, environmental friendliness, and economic efficiency. The information-perception technology--a key aspect of intelligent ships--has been developed to ensure that ships can obtain accurate information regarding themselves as well as their surrounding environment, including neighboring ships, video-surveillance information, and obstacle information. The purpose of ship classification is to identify accurately ship classes within ship datasets. Since visual sensors are often susceptible to weather changes and illumination conditions, ship classescan, at times, be difficult to identify. Moreover, intra-class variations within certain ship classes make ship classification more complex and challenging [4; 5]. Generated from different sensors, ship images can be classified into synthetic aperture radar (SAR) images, infrared images, and visible optical images respectively. SAR images can be generated at all day/night time and under weather conditions (clear or cloudy), but SAR images have some limitations. SAR images mainly obtained from a radar, which is expensive and vulnerable to other electromagnetic interference. SAR images with low resolution do not facilitate the subsequent target tracking, and the object detection methods based on SAR images can only distinguish large vessels from background, but ignoring the boats with a long distance. In addition, using radar would give away the vessel's position, which precludes its use in some military applications. Infrared images are monitored by thermal imaging instead of natural light. However, infrared images have high sensitivity to the thermal radiation of the target, but not sensitive to the brightness change. Moreover, with low spatial resolution, infrared images cannot provide texture details. Dealing with targets with long distance, infrared images have a poor signal-to-noise ratio, seriously affecting the efficiency. By contrast, visible optical images contain grayscale information of multiple bands and have characteristics of high resolution and detailed texture. Therefore, visible optical images can provide abundant visual information and have better discrimination of targets. Using an appropriate algorithm, more ship features can be extracted to facilitate further ship classification and detection. Even more noteworthy is that the camera used to obtain the visible optical images has a low cost and power consumption, in addition, it can be implemented easily thanks to the small size. Owing to the high importance associated with ship recognition and classification over the past few decades, several investigations have been performed in this regard by a number of researchers, and numerous approaches for ship classification have been proposed [6; 7]. The traditional classification method is based on handcrafted features, and it involves use of a combination of target features, such as contour, texture, area, and color, for class recognition. Several feature-extraction techniques associated with face recognition have also been employed to facilitate class recognition within certain sample ship datasets [8]. Among traditional feature-description methods, the ones commonly used include the histogram of oriented gradients (HOG) [9], scale-invariant feature transform (SIFT) [10], local binary patterns (LBP) [11], etc. Many research works have been done on ship classification. [PERSON] et al.