  Automatic Target Recognition (ATR) in Synthetic aperture radar (SAR) images
becomes a very challenging problem owing to containing high level noise. In
this study, a machine learning-based method is proposed to detect different
moving and stationary targets using SAR images. First Order Statistical (FOS)
features were obtained from Fast Fourier Transform (FFT), Discrete Cosine
Transform (DCT) and Discrete Wavelet Transform (DWT) on gray level SAR images.
Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM)
and Gray Level Size Zone Matrix (GLSZM) algorithms are also used. These
features are provided as input for the training and testing stage Support
Vector Machine (SVM) model with Gaussian kernels. 4-fold cross-validations were
implemented in performance evaluation. Obtained results showed that GLCM + SVM
algorithm is the best model with 95.26% accuracy. This proposed method shows
that moving and stationary targets in MSTAR database could be recognized with
high performance.
