Automatic classification of targets in synthetic aperture radar imagery using topographic features

Abstract

Automatic classification of target in synthetic aperture radar (SAR) imagery is performed using topographic features. Targets are segmented from wide area imagery using a constant false alarm rate (CFAR) detector. Individual target areas are classified using the topographical primal sketch which assigns each pixel a label that is invariant under monotonic gray tone transformations. A local surface fit is used to estimate the underlying function oat each target pixel. Pixels are classified based on the zero crossings of the first directional derivatives and the extrema of second directional derivatives. These topographic labels along with the quantitative values of second directional derivative extrema and gradient are used in target matching schemes. Multiple matching schemes are investigated including correlation and graph matching schemes that incorporate distance between features as well as similarity measures. Cost functions are tailored to the topographic features inherent in SAR imagery. Trade offs between the different matching schemes are addressed with respect to robustness and computational complexity. Classification is performed using one foot and one meter imagery obtained from XPATCH simulations and the MSTAR synthetic dataset.