Synthetic Aperture Imaging Radiometers Research Articles

Impact of the Antenna Spacing on the Brightness Temperature Maps Retrieved with a Synthetic Aperture Imaging Radiometer

For almost five years, phase 0 and phase A studies have been conducted by the French space agency for a second generation of the Soil Moisture and Ocean Salinity (SMOS) satellite devoted to a High Resolution (HR) follow-on mission. Within the frame of the preliminary results obtained with a candidate array for this SMOS-HR project, this contribution focuses on what could happen to any synthetic aperture imaging radiometer when the shortest spacing between the antennas of the interferometric array becomes smaller than a geometrical limit below which the synthesized field of view seems to be wider than the field of view seen by each elementary antenna. It is shown that in such a situation, the inversion of the complex visibilities becomes unstable in presence of noise and this instability is characterized by the undesirable presence of a phantom in the retrieved brightness temperature maps. The origin of this phantom is explained and a solution to cure the interferometric array from that issue is proposed and assessed with the aid of numerical simulations.

Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers

Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method.

Reduction of the Reconstruction Error With Lower and Upper Bounds in Synthetic Aperture Imaging Radiometers

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution Earth observation by use of small-aperture antennas sparsely arranged to achieve a large-aperture antenna. High-precision reconstruction algorithm is one of the key contents of SAIRs. Owing to the ill-posed problem and band-limited physical characteristic, there is a still large residual error for traditional regularization methods. It should be noted that the prior information like the lower and upper bounds of the brightness temperature distributions has not been utilized in the reconstruction procedure, especially for the open ocean with relatively small brightness temperature difference. In order to reduce the reconstruction error in SAIRs, a reconstruction method based on active set algorithm is presented by solving the least squares problems with lower and upper bounds. The simulation experiment results show that the proposed method can more effectively reduce the reconstruction error and better improve the accuracy of retrieved brightness temperature distributions in SAIRs than the band-limited regularization method, demonstrating the effectiveness of the proposed method.

Bayesian Inference for Inversion in Synthetic Aperture Imaging Radiometry

The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

Adaptive CLEAN algorithm for millimetre wave synthetic aperture imaging radiometer in near field

High-resolution millimetre wave images of contiguous targets often suffer from the influence of sidelobe artefacts, partial correlation between targets and so on. Owing to the characteristics of near-field synthetic aperture imaging radiometers [such as the changing point spread function (PSF) and slender sideline], the existing CLEAN algorithms are unsuitable for near-field synthetic aperture imaging. This study is devoted to establishing a novel CLEAN algorithm (named adaptive CLEAN) to clean the reconstructed millimetre wave images accurately. First, the characteristics of near-field synthetic aperture imaging are studied. Then, the adaptive CLEAN algorithm is established based on these characteristics. In this study, the authors amend the amplitude of the targets and select the matching PSF for them according to their azimuths. Unlike other CLEAN algorithms, the parameters of this adaptive CLEAN algorithm are calculated by a formula, which is concluded from a lot of simulation experiments. Finally, the effectiveness of the proposed adaptive CLEAN algorithm is tested by several simulation experiments, and the superiority is also demonstrated by comparing it with the existing CLEAN algorithm. The results demonstrate that the proposed method is an efficient, feasible algorithm for cleaning the reconstructed images, irrespective of the point or contiguous targets.

A New 2-Dimensional Millimeter Wave Radiation Imaging System Based on Finite Difference Regularization

Synthetic aperture imaging radiometer (SAIR) has the potential to meet the spatial resolution requirement of passive millimeter remote sensing from space. A new two-dimensional (2-D) imaging radiometer at millimeter wave (MMW) band is described in this paper; it uses a one-dimensional (1-D) synthetic aperture digital radiometer (SADR) to obtain an image on one dimension and a rotary platform to provide a scan on the second dimension. Due to the ill-posed inverse problem of SADR, we proposed a new reconstruction algorithm based on Finite Difference (FD) regularization to improve brightness temperature images. Experimental results show that the proposed 2-D MMW radiometer can give the brightness temperature images of natural scenes and the FD regularization reconstruction algorithm is able to improve the quality of brightness temperature images.

System Simulation Modeling for Near-field Millimeter Wave Synthetic Aperture Imaging Radiometer

Due to the fact that theoretical analysis and instrument construction for antenna array of synthetic aperture imaging radiometer (SAIR) are both complicated, and designers usually hope to predict the imaging effect and analysis the influence of relevant parameters of SAIR before the system design. The imaging simulation is a very useful method in the system design of SAIR. This paper is devoted to establishing an accurate imaging model for simulating the process of near-field millimeter wave SAIR. In this model, the target radiation signals and received signals of receivers are represented by the accurate signals in the time domain, which improves the efficiency of this simulation model significantly. The visibility function is collected by the cross-correlation between I/Q signals of the antenna pairs just as the imaging process of the practical SAIR. Some characteristics (such as the coherence between targets, the resolution and no-aliasing FOV of SAIR and so on) are verified by the corresponding 1D and 2D simulation experiments, and the effectiveness of this imaging model is also tested by these simulation experiments. The simulation experiment results show that this model is an efficient, accurate imaging simulation model, and can be employed in the system design of near-field millimeter wave SAIR.

The CS-Based Imaging Algorithm for Near-Field Synthetic Aperture Imaging Radiometer

The CS-Based Imaging Algorithm for Near-Field Synthetic Aperture Imaging Radiometer

AN ACCURATE IMAGING ALGORITHM FOR MILLIMETER WAVE SYNTHETIC APERTURE IMAGING RADIOMETER IN NEAR-FIELD

Due to the fact that the imaging distance is similar to the dimension of synthetic aperture antenna in near-fleld, the Fourier imaging theory used in the traditional synthetic aperture imaging radiometer (SAIR), which is based on the far-fleld approximation, is invalid for near-fleld synthetic aperture imaging. This paper is devoted to establishing an accurate imaging algorithm for near- fleld millimeter wave SAIR. Firstly, the near-fleld synthetic aperture imaging theory is deduced and its relationship to the far-fleld imaging theory analyzed. Then, an accurate imaging algorithm based on the near-fleld imaging theory is established. In this method, the quadratic phase item and antenna pattern are taken into consideration, and the image reconstruction is performed by minimizing the Total- Variation norm of brightness temperature image, which reduces the in∞uence of the visibility observation error and improves imaging precision. Finally, the efiectiveness of the proposed imaging algorithm has been tested by means of several simulation experiments, and the superiority is also demonstrated by the comparison between it and the existing Fourier transform methods. The results demonstrate that the proposed method is an e-cient, feasible imaging algorithm for near- fleld millimeter wave SAIR.

A Robust Regularization Kernel Regression Algorithm for Passive Millimeter Wave Imaging Target Detection

This letter deals with small target detection in passive millimeter wave (PMMW) imaging. Specifically, it focuses on a general detection scheme, where, first, the background is suppressed through a background prediction algorithm, and then the detection is accomplished. A precise prediction of the background is essential to a successful outcome. In practical applications, background estimation problem is more suitable to be considered as a nonlinear regression problem. Kernel methods are effective to solve the nonlinear problem. To improve the accuracy of the background prediction with kernel methods, we utilize robust loss function, to tolerate the noise outliers, and regularization methods, to avoid overfitting of the data. Experiments are conducted on PMMW images collected by a synthetic aperture imaging radiometer. The results demonstrate the effectiveness of the proposed algorithm.

NUFFT-Based Iterative Reconstruction Algorithm for Synthetic Aperture Imaging Radiometers

Synthetic aperture imaging radiometers (SAIRs) are powerful sensors for high-resolution observation of the Earth at low microwave frequencies. However, the future application of this new technology is limited by its large number of element antennas and receiving channels. In order to further reduce the complexity of the whole system, the circular and rotating array configurations have been proposed. Their disadvantages lie primarily in more complicated image reconstruction, because the conventional fast Fourier transform (FFT) method could not be used directly for image reconstruction. This letter introduces an iterative method for the reconstruction of radiometric brightness temperature maps from nonuniform visibility function samples. This method combines the conjugate gradient algorithm with min-max nonuniform FFT approach, which has been shown to provide considerably improved image quality relative to the traditional gridding method. Simulations for SAIRs were performed and showed better image quality in comparison to the traditional frequency interpolation method.

Regularized image reconstruction for the SMOS space mission

Synthetic Aperture Imaging Radiometers (SAIR) are powerful sensors for high-resolution observations of the Earth at low microwaves frequencies. Within this context, the European Space Agency is currently developing the SMOS mission devoted to the monitoring of Soil Moisture and Ocean Salinity at global scale from L-band space borne radiometric observations obtained with a two-dimensional interferometer. This contribution is concerned with the reconstruction of radiometric brightness temperature maps from dual-polarimetric interferometric measurements.

Brightness Temperature Map Reconstruction from Dual-Polarimetric Visibilities in Synthetic Aperture Imaging Radiometry

Synthetic aperture imaging radiometers are powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the soil moisture and ocean salinity (SMOS) mission devoted to the monitoring of SMOS at global scale from L-band spaceborne radiometric observations obtained with a 2-D interferometer. This paper is concerned with the reconstruction of radiometric brightness temperature maps from interferometric measurements. More exactly, it extends the concept of ldquoband-limited resolving matrixrdquo to the case of the processing of dual-polarimetric data.

On the Reduction of the Reconstruction Bias in Synthetic Aperture Imaging Radiometry (Corrected)$^{\ast}$

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper is concerned with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. Even in the absence of modeling errors and radiometric noise, a systematic error, or bias, has been observed in the reconstructed maps. The origin of this bias is analyzed and an efficient solution is proposed for reducing it. The core reconstruction procedure is not changed, and no additional measurements are needed. Throughout the scientific rationale, particular emphasis is laid on numerical simulations carried out for the Soil Moisture and Ocean Salinity space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low-orbit platform

On the Reduction of the Reconstruction Bias in Synthetic Aperture Imaging Radiometry

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper is concerned with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. Even in the absence of modeling errors and radiometric noise, a systematic error, or bias, has been observed in the reconstructed maps. The origin of this bias is analyzed and an efficient solution is proposed for reducing it. The core reconstruction procedure is not changed, and no additional measurements are needed. Throughout the scientific rationale, particular emphasis is laid on numerical simulations carried out for the Soil Moisture and Ocean Salinity space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low-orbit platform

A resolving matrix approach for synthetic aperture imaging radiometers

Synthetic aperture imaging radiometers (SAIRs) are potential powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper deals with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. It is demonstrated that the corresponding inverse problem is not well-posed and must, therefore, be regularized in order to provide a unique and stable solution. A new approach is presented by referring to the notion of modeling operator and to the concept of a resolving matrix of the instrument. To illustrate the theory, numerical simulations are carried out in reference to the Soil Moisture and Ocean Salinity space mission led by the European Space Agency. The results are discussed with emphasis on stability and error analysis.

Apodization functions for 2-D hexagonally sampled synthetic aperture imaging radiometers

It is now well established that synthetic aperture imaging radiometers promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the Soil Moisture and Ocean Salinity (SMOS) mission. The Y-shaped array selected for SMOS is fitted with equally spaced antennae and leads to a natural hexagonal sampling of the Fourier plane. This paper deals with the choice of the apodization function to be applied to the complex visibilities. The aim of this function is to reduce the Gibbs phenomenon produced by the finite extent of the star-shaped frequency coverage and the resulting sharp frequency cut-off. A large number of windows are introduced. A comparison of these in terms of their spatial domain properties is given, according to criteria relevant for remote sensing of the Earth's surface. This paper also describes how discrete Fourier transform calculations over hexagonal grids can be performed using a simple algorithm. Actually, standard fast Fourier transform algorithms designed for Cartesian grids and which have a long track record of optimization can be reused. Finally, an interpolation formula is given for resampling data from hexagonal grids without introducing any aliasing artifacts in the resampled data.