Automatic Target Recognition (ATR) from SAR Imaginary by Using Machine Learning Techniques

TL;DR

This paper presents a machine learning approach using SVM and various feature extraction techniques to accurately recognize moving and stationary targets in SAR images, achieving over 95% accuracy.

Contribution

It introduces a novel combination of statistical and texture features with SVM for ATR in SAR images, demonstrating high recognition performance.

Findings

GLCM + SVM achieved 95.26% accuracy

The method effectively distinguishes moving and stationary targets

High performance recognition on MSTAR database

Abstract

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.