Texture modeling and validation using recorded high resolution sea clutter data

Abstract

We consider modeling and estimating the texture in high-resolution non-Gaussian sea clutter. The cyclostationarity of sea clutter is investigated and validated by processing measured high-resolution data. The clutter is modeled as a compound Gaussian process and the texture as the superposition of real cosines with unknown frequencies, amplitudes, and phases. We propose a method for estimating the model parameters and retrieving the texture component from the intensity data in the presence of multiplicative noise (the speckle) with unknown power spectral density. The method exploits the clutter cyclostationarity and is based on a relaxation optimization approach. The ability of the proposed method to retrieve texture information is investigated by processing simulated and measured sea clutter data.