Machine learning (ML) is the computational study of algorithms that improve performance based on experience learned from examples. Since machine learning techniques provide 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 a pattern recognition task. Both learn the 2D signal introduced from the MSTAR SAR (Synthetic Aperture Radar) image 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 (+or-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 the input vector from 465 extracted features down to 30 features. We report three experimental results: DT to learn from the original 465 features without using PCA; DT algorithm with the use of PCA for reducing input dimensionality; and 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.


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    Title :

    Combat vehicle classification using machine learning


    Contributors:
    Zeng, H. (author) / Huang, J. (author) / Liang, Y. (author)


    Publication date :

    1999


    Size :

    6 Seiten, 10 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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