Combat vehicle classification using machine learning

H. Zeng; J. Huang; Y. Liang

doi:10.1117/12.339808

29 January 1999 Combat vehicle classification using machine learning

H. Zeng, J. Huang, Y. Liang

Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339808
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States

Abstract

Machine Learning (ML) is the computational study of algorithms that improve performance based on experience learned from examples. Since machine learning technique provides new learning methodologies capable of dealing with the complexities of input signals (imagery), pattern recognition investigates the applicability of modern machine learning methods to develop recognition systems with learning capabilities. This paper introduces two machine learning techniques-Algorithm Quasi-optimal (AQ) and Decision Tree (DT) as the classifiers for undertaking pattern recognition task. Both learn the 2D signal introduced from MSTAR SAR (Synthetic Aperture Radar) imagery database consisting of three classes of combat vehicles-BMP- 2, BTR-70, and T-72 tank. 67 images drawn from the database with similar aspect (+/- 15 degrees) are used for training the classifiers while unseen 47 images are used for testing. Principle Component Analysis (PCA) method and whitening transformation are used to reduce the dimensionality of input vector from 465 extracted features down to 30 features. We report three experimental results-(1) DT to learn from the original 465 features without using PCA, (2) DT algorithm with the use of PCA for reducing input dimensionality, and (3) AQ algorithm to learn the input features with PCA. The results show that the AQ has better performance than DT in terms of faster learning and higher recognition accuracy when the PCA and whitening transformation are applied.

Citation Download Citation

H. Zeng, J. Huang, and Y. Liang "Combat vehicle classification using machine learning", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339808

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