Open AccessAutomatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features
Author(s) -
Amoon Mehdi,
Rezairad Gholamali
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0027
Subject(s) - artificial intelligence , automatic target recognition , computer science , synthetic aperture radar , pattern recognition (psychology) , feature extraction , zernike polynomials , computer vision , robustness (evolution) , radar imaging , histogram , preprocessor , histogram of oriented gradients , support vector machine , radar , image (mathematics) , telecommunications , physics , wavefront , optics , biochemistry , chemistry , gene
In the present study, a new algorithm for automatic target detection (ATR) in synthetic aperture radar (SAR) images has been proposed. First, moving and stationary target acquisition and recognition image chips have been segmented and then passed to a number of preprocessing stages such as histogram equalisation, position and size normalisation. Second, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and robustness in the presence of the noise has been introduced for the first time. Third, a genetic algorithm‐based feature selection and a support vector machine classifier have been presented to select the optimal feature subset of ZMs for decreasing the computational complexity. Experimental results demonstrate the efficiency of the proposed approach in target recognition of SAR imagery. The authors obtained results show that just a small amount of ZMs features is sufficient to achieve the recognition rates that rival other established methods, and so ZMs features can be regarded as a powerful discriminatory feature for automatic target recognition applications relevant to SAR imagery. Furthermore, it can be observed that the classifier performs fairly well until the signal‐to‐noise ratio falls beneath 5 dB for noisy images.