Nodal sampling: a new image reconstruction algorithm for SMOS

Soil moisture and ocean salinity (SMOS) brightness temperature (TB) images and calibrated visibilities are related by the so-called G -matrix. Due to the incomplete sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the TBs to diminish the amplitude of artifacts such as ripples, as well as other Gibbs -like effects. In this paper, a novel image reconstruction algorithm focused on the reduction of Gibbs -like contamination in TB images is proposed. It is based on sampling the TB images at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a significant reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This technique has been thoroughly validated using snapshots over the ocean, by comparing TBs reconstructed in the standard way or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the difference between the measurement and the model is reduced around 1 K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.7 K when considering the global ocean. This represents a crucial improvement in TB quality, which will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity.Peer ReviewedPostprint (author's final draft

Moving and stationary target acquisition radar image enhancement through polynomial windows

The Fourier transform involved in synthetic aperture radar (SAR) imaging causes undesired sidelobes which obscure weak backscatters and affect the image clarity. These sidelobes can be suppressed without deteriorating the image resolution by smoothing functions known as windowing or apodization. Recently, the theory of orthogonal polynomials has gained considerable attention in signal processing applications. The window functions that are derived from the orthogonal polynomials have interesting sidelobe roll-off properties for better sidelobe apodization, hence it can be used for radar image enhancement. In this work, a new window is constructed from Jacobi orthogonal polynomials and its performance in SAR imaging is analyzed and compared with commonly used window functions. Also, apodization functions involved in Fourier transform harmonic analysis and Fourier transform spectroscopy are discussed in the context of SAR imaging