A Transfer Learning Method For Ship Recognition In Multi-Optical Remote Sensing Satellites

In this paper, a transfer learning method of ship target recognition is proposed. This method aims at identifying unlabeled ships in high-resolution, based on the theory of transfer learning, assisting with a number of ship samples with different resolutions from different satellites. In the traditional machine learning method, training data and test data are assumed to have the same distribution. However, because of the different distributions and spaces in most cases, such as images from different satellites, the accuracy rate will decline. In this paper, we proposed a method that aligns the distributions as well as the subspace bases to solve this problem. This paper first proposed Adaptation Local Linear Embedding (ALLE) algorithm to achieve space alignment and then aligned both marginal distribution and conditional distribution by Joint Distribution Adaptation (JDA). This paper focuses on the identification of three types of ships which are destroyers, cruisers, and aircraft carriers basing on the method proposed (ALLE-JDA), using the knowledge of source samples low-resolution labeled ships to help identify the unlabeled high-resolution samples. The experimental results show that this method is better than several state-of-the-art methods.

[1]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[2]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Wei Zhang,et al.  Ship target segmentation and detection in complex optical remote sensing image based on component tree characteristics discrimination , 2012, Photonics Asia.

[4]  Tatsuya Harada,et al.  Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.

[5]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[6]  Goktug Kagan Yuksel,et al.  Ship recognition and classification using silhouettes extracted from optical images , 2016, 2016 24th Signal Processing and Communication Application Conference (SIU).

[7]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[8]  Baojun Zhao,et al.  Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Yingjun Zhang,et al.  Ship target classification based on Hu invariant moments and ART for maritime video surveillance , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).

[10]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[11]  Tao Jiang,et al.  Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges , 2017, IEEE Wireless Communications.

[12]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[13]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Qilong Wang,et al.  Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Bo Li,et al.  Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Katia Estabridis.  Joint ship classification and learning , 2015, 2015 IEEE International Symposium on Technologies for Homeland Security (HST).