Abstract
The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.
Highlights
The classification of marine vessels is an important issue in maritime safety and traffic control
In 2010, Zhu et al [4] conducted experiments on spaceborne optical images (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
The method maintain theThe internal structure ofonly the as follows: (1) we propose a feature structure fusioncan method based on Linear discriminant analysis (LDA)
Summary
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]. 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). The number of SAR sensors is limited, the revisit period is long, and the resolution is low. In 2010, Zhu 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. The visible image has sufficient detail and color information, and the
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