Comparison of selected features for target detection in synthetic aperture radar imagery

Several methods are available that capture the statistics of radar imagery. The best features, in the sense of man-made target discrimination, are expected to be different for different types of natural background, and for different objects of interest such as vehicles. We demonstrate that discrimination of natural background and man-made objects using low resolution synthetic aperture radar imagery is possible using multiscale autoregressive (MAR), multiscale autoregressive moving average (MARMA) models, and singular value decomposition (SVD) methods. We use the model coefficients, moments of the model residual vectors, a subset of eigenvectors, and moments of the selected eigenvectors, as features for target discrimination. All the test imagery used here was 1.5 metre resolution.