NDLI: Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features

Content Provider	IET Digital Library
Author	Amoon, Mehdi rad, Gholam ali Rezai
Abstract	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.
Starting Page	77
Ending Page	85
Page Count	9
ISSN	17519632
Volume Number	8
e-ISSN	17519640
Issue Number	Issue 2, Apr (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/8/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0027
Journal	IET Computer Vision
Publisher Date	2013-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ATR Automatic Target Detection Automatic Target Recognition Application Computational Complexity Electrical Engineering Computing Feature Extraction Genetic Algorithm Genetic Algorithm-based Feature Selection Histogram Equalisation Image Recognition Invariance Robustness Knowledge Engineering Technique Linear Transformation Invariance Property Moving Target Acquisition Noisy Image Optimal Feature Subset Optimal Selection Optimisation Technique Position Normalisation Preprocessing Stages Radar Computing Radar Equipment Radar Imaging Recognition Image Chips SAR Image Signal to Noise Ratio Size Normalisation Stationary Target Acquisition Support Vector Machine Support Vector Machine Classifler SVM Synthetic Aperture Radar Synthetic Aperture Radar Image System And Application Zernike Moments Feature ZM Feature
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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