We evaluate a recently reported algorithm for computing frequency-dependent radar imagery in scenarios relevant for performing spectral feature identification. For each image pixel in the spatial domain a computed frequencydependent reflectivity is used to produce a corresponding spectral feature identification. We show that this novel image reconstruction technique is capable of considerable flexibility for achieving fine spectral resolution in comparison with previous techniques based on conventional synthetic aperture radar (SAR), yet new challenges are introduced with regard to achieving fine range resolution.

Abstract-We investigate the feasibility of using synthetic aperture radar (SAR) data to identify materials at each pixel in a SAR image. A fundamental concept underlying our approach is to extract the dispersion of the reflectivity at each pixel by dividing the data into several frequency sub-bands. We first compute synthetic radar data using parameters that are characteristic of a typical wide-band SAR system operating in a spotlight mode and illuminating several scattering regions that differ in their frequency response. Secondly, we process the data by subdividing the full data set into frequency sub-bands thereby extracting the dispersion at each pixel. Third and finally, we perform the material identification using a rudimentary classification analysis. The approach described herein offers a new method for planning experimental data collections for the purpose of material identification through SAR image formation.