Effect of Spatially Variant Apodization on SAR Image Classification

This paper investigates the effect of the application of Spatially Variant Apodization techniques to SAR images on the statistical properties of the apodized radar signal and on the capability of extracting information from apodized SAR images. After deriving the statistical model of the apodized image (in terms of both probability density function and moments) two new classification schemes of homogeneous regions with different radar cross section in apodized SAR images are obtained. The performance of the new schemes are deeply investigated and compared with the performance achievable by Maximum Likelihood classification schemes developed under the assumption of Gaussian statistics and applied to the original images and to the apodized images. The performance analysis shows that the new schemes maintain the information extraction capabilities while at the same time allowing the sidelobe level to be reduced and the mainlobe resolution to be preserved.