# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 1 Introduction
Literature [16] has trained extreme learning machine for exploiting the correlation of multi-color features. Literature [17] has extracted features from a pre-trained 16-layer convolutional neural network (vgg-16) and train the logistic regression classifier for object recognition. Literature [18] has constructed a dual-flow DNN network to extract the high-frequency and low-frequency features of ship images, and finally ELM aggregates feature and decision-making. Literature [19] has proposed a classification framework consists of a multi-feature ensemble based on convolutional neural network (ME-CNN). Literature [20] has introduced a new approach based on ELM to learn discriminative CNN features. Compared with the method based on traditional features, the method based on CNN shows powerful capabilities of feature extraction. Considering the fact that a single feature may not be comprehensive enough for representing an image, some scholars have made further explorations on feature fusion. Some existing feature fusion methods mostly have used simple concatenation of different features in series [12; 13], which do not consider the heterogeneous characteristics between different features. [PERSON] et al. [21] have adopted Canonical Correlation Analysis (CCA) for feature fusion to achieve compression of feature vector dimensions. Subsequently, KCCA (Kernel CCA) [22] and OCCA (Orthogonal CCA) [23] were presented for feature fusion. [PERSON] et al. [24; 25; 26; 27] proposed a multi-feature structure fusion method, which achieved good recognition results in many fields. The method first constructs the internal structure of each feature by the similarity measure, and then performs algebraic operations on the corresponding structure and features based on locality preserving projection (LPP) [28]. The method projects features from the combined high-dimensional space to the low-dimensional space. This method not only retains the internal structure of the features, but also greatly reduces the dimension of the features and improves the performance of object classification. Although the method of structural fusion can fuse different features together, this method belongs to unsupervised learning method. The weakness of this method is that the feature's category information is not integrated into the process of feature fusion, so the natural distribution structure of the class from multi-feature is usually ignored. The structural information can enhance the discrimination of object classification. To solve this issue, a novel multi-feature structure fusion based on spectral regression discriminant analysis (SF-SRDA) is proposed in this paper by combining structural fusion [24] with linear discriminant analysis (LDA) [29; 30]. SF-SRDA is a supervised dimension reduction technology, so that the method can not only preserve the internal structure of the category information in the process of feature fusion, but also select the minimal dimension features. The minimal dimension features can completely describe the target for object recognition and classification. The overall framework of the method is shown in Figure 1 (In this paper, two-type features indicate multi-feature for structure fusion), and the focus (which will be detailed in Section 3) of this paper is marked with a red box. The main contributions of this paper can be summarized as follows: (1) we propose a feature structure fusion method based on LDA. The method can not only maintain the internal structure of the feature, but also integrate the category information to improve recognition performance. (2) Due to the consideration of category information and the intrinsic structure, the feature dimension can be greatly reduced from 102,400 to 5 dimensions. (3) The experimental results are promising for marine vessels classification. The following sections are arranged as follows. Section 2 is the selection of multiple features. Section 3 describes the construction of the internal structure of features and the structure fusion mechanism of the feature.