Simulated ship recognition using two-dimensional PCA

Guangzhou Zhao; Guangxi Zhu; Feng Peng; Shuwen Wang; Huazhong Xu

doi:10.1117/12.749331

15 November 2007 Simulated ship recognition using two-dimensional PCA

Guangzhou Zhao, Guangxi Zhu, Feng Peng, Shuwen Wang, Huazhong Xu

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 678622 (2007) https://doi.org/10.1117/12.749331
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

This paper proposes a fast and robust algorithm for classification and recognition of ships based on the two-dimensional Principal Component Analysis (2DPCA) method. The three-dimensional ship models achieve by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The 2DPCA method as against conventional PCA method for simulated ship recognition using training and testing experiments, as the training and testing sample size is large, and there are great variations in different azimuth and elevation for ship viewpoints. The experiment of ship recognition using the global feature of ships is not satisfied with us, so we proposed an improved 2DPCA method based on the local feature of ships. Some recognition results from simulated data are presented, it shows that the improved 2DPCA method outperform PCA in ship recognition and also superior to PCA in terms of computational efficiency for feature extraction. So our method is more preferable for ship classification and recognition.

Citation Download Citation

Guangzhou Zhao, Guangxi Zhu, Feng Peng, Shuwen Wang, and Huazhong Xu "Simulated ship recognition using two-dimensional PCA", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678622 (15 November 2007); https://doi.org/10.1117/12.749331

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KEYWORDS

Principal component analysis

3D modeling

Feature extraction

Detection and tracking algorithms

Databases

Computer simulations

Data modeling

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