A transfer learning method of ship identification based on weighted HOG features

A transfer learning method based on Weighted Histogram of Oriented Gradient (WHOG) features for ships identification is proposed, which uses labeled ships at different resolutions to identify fixed resolution ships. An improved HOG features called WHOG is presented, which have a better description of the contours on different types of ships. The training and the test samples at different resolutions obey different distributions. The JDA method considers probabilistic adaptation without performing spatial alignment. To solve this problem, Mapped Alignment-Joint Distribution Adaptation (MA-JDA) method is proposed. MA is utilized to map the source and target domain data to the same feature space, then JDA is utilized to perform probabilistic adaptation to improve transfer learning performance. Extensive experiments demonstrate that the superiority of WHOG features over traditional HOG features and the MA-JDA method is better than several state-of-the-art transfer learning methods.