The two-scale model (TSM) has been widely used to calculate scattering from surfaces with multiple roughness scales. In our study, we derived an improved TSM based on the angular composite model (ACM), which expresses the calculation of the scattering coefficient as an integral over the surface angles, making it possible to accelerate the process by combining with Monte Carlo integration. In addition, any method can be employed to calculate the small-scale scattering. We use the first-order small-slope approximation model (SSA1) with wider applicability instead of the small perturbation model (SPM) to calculate the scattering contribution from small-scale rough surfaces. We analyze the roughness of short waves on anisotropic wind-driven sea surfaces, give numerical examples of horizontally polarized backscattering and bistatic scattering, taking into account the L-band and different wind speeds, and compare them with the measurement data, classic TSM (KA + SPM), facet-based TSM (KA + SSA1), closed-form two-scale model [bistatic anisotropic polarimetric TSM (BA-PTSM)], and second-order small-slope approximation model (SSA2) algorithms. The proposed bistatic scattering model is in general agreement with the classic model and is demonstrated to be more consistent with measurements at larger scattering angles and more insensitive to the cutoff wavenumber.

Speckle noise, a common artifact inherent in synthetic aperture radar (SAR) imagery, significantly degrades image quality. This deterioration in quality impedes critical tasks such as segmentation, object recognition, and other related applications. This paper introduces an innovative iterative trilateral filtering approach for SAR imagery enhancement, which is distinct in its use of Haar wavelet coefficients combined with pixel-domain distance confidence levels. By incorporating nonlocal (NL) information, the proposed approach aims to augment conventional wavelet transforms, effectively utilizing imprecise pixel data through confidence-weighted aggregations. The method uniquely integrates transform-domain, spatial-domain, and statistical properties of SAR images, which significantly enhances image clarity by producing high-fidelity despeckled outputs. Compared with traditional wavelet transforms, the proposed approach focuses on the discreteness analysis of inter-patch wavelet coefficients, which allows for deeper insights and better preservation of SAR image structures. The algorithm computes single-level Haar wavelet coefficients for patch pairs and determines wavelet weights based on inter-coefficient distances. Then, the algorithm calculates guided image pixel distances and reduces patch-wise Bhattacharyya distances to encode pixel distance confidence. These three weights are then consolidated for effective despeckling. To address directional blurring due to SAR image anisotropy, the conventional NL method’s square pixel vector is replaced with circular and annular vectors. The proposed approach demonstrates significant improvements in image integrity preservation, reducing speckle noise, as evidenced in our experiments with both simulated and real datasets.

Multi-channel moving target detection algorithm is an essential technique for ground moving target indication (GMTI). The inconsistency of magnitude and phase characteristics between channels will affect the clutter suppression effect of the Synthetic Aperture Radar (SAR) system, and thus affect the final performance of moving target detection. To solve this problem, a channel balance algorithm based on the two-dimensional Gaussian kernel function [1] and the non-local means (NLM) filter is proposed in this paper. Firstly, the phase error is estimated and compensated by the two-dimensional Gaussian kernel function in the image domain, and then the magnitude error is compensated by the mean filtering. Finally, the NLM filter is applied to further suppress clutter. The proposed algorithm can effectively eliminate magnitude and phase errors, and improve the coherence between different channels. Experimental results on real airborne SAR data demonstrate the effectiveness of our algorithm.

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

The inherent magnitude and phase errors between channels in the multichannel synthetic aperture radar/ground moving target indication (SAR/GMTI) system will affect the clutter suppression and moving target detection performance of the system. To solve this problem, this letter proposes an adaptive channel balancing algorithm based on the 2-D Gaussian kernel function. First, the iterative reweighted least squares (IRLS) algorithm is used to estimate and compensate the inherent linear phase error that varies with the Doppler frequency in the range-Doppler domain. Then the Gaussian statistical analysis of the residual phase errors is performed in the image domain after the azimuth processing. According to the coupling characteristics of the range and azimuth of the SAR image, the correlation coefficient and the standard deviations of the phase errors in the range and azimuth directions are computed, and a 2-D Gaussian kernel function is constructed to estimate and compensate the residual phase errors. Finally, a mean filter is used to compensate the 2-D magnitude errors. This algorithm can eliminate the magnitude and phase errors and improve the coherence between different channels. The real airborne SAR data demonstrate the effectiveness of the proposed algorithm.

Based on a joint analysis of linear Kelvin wake kinematics and water dispersion relation, the features of several components of ship wake are identified in the spectral domain, such as the “X”-shaped Kelvin wake, the narrow “X”-shaped solitary wave packets, and the cross-shaped turbulent and near-field waves. Alternatively, these components can be separately reconstructed in spatial domain using the inverse Fourier transformation. These relations are verified through numerical simulation of the wakes of a ship moving at different speeds. This wake decomposition is now extended to wake feature analysis of real synthetic aperture radar (SAR) image. It reveals that although the images of ship wake have been modulated by SAR imaging mechanisms in various aspects, their spectral characteristics are closely analogous to that of wake surface elevation. Taking advantage of the loci and shape of the wake spectrum, the transverse wave, the divergent wave, the turbulent wave, and the solitary wave packets can be isolated from the original SAR image with full wake appearance. The reconstructed images of wake components facilitate the further estimation of the direction, speed, length, hull geometry, and propulsion system of the ship. This decomposition can also recover wake components from multiple ship wakes and provide an understanding of their roles on SAR image.

Imaging distortions induced by each particular rotation of a moving ship are quantitatively investigated through numerical simulations. Two sets of dynamics are considered in the model, namely a ship with pitch, yaw, and roll rotations, and the time evolutions of its wake. To construct high-resolution synthetic aperture radar (SAR) images in spotlight mode, the time-varying scattering from the electrically very large scene is computed by a parallel quasi-stationary algorithm, in which the physical optics (PO) method is used to compute the scattering from the ship, and a PO phase correction of the two-scale model is used to take account of the Doppler effects caused by the wake. Reasonable agreement is obtained when comparisons are made between the simulated and real SAR images. A range of imaging distortions are observed and analyzed, such as the displacement, rotation, stretching/compressing, and broaden/narrow of the ship image. A systematic analysis shows that these distortions can be characterized by four main types of transformations, namely translation, rotation, scaling, and shearing. This paper presents quantitative insights into the data interpretation and signature classification of ship on SAR image.

The domain decomposition method (DDM) and finite-element method (FEM) are developed for numerical solution of bistatic scattering from the composite model of a ship on and a target above a two-dimensional (2-D) randomly rough sea surface under an electromagnetic (EM) wave incidence at low grazing angle. The coupling boundary conditions on the interface between two adjacent subdomains are derived when the conformal perfectly matched layer is used as the truncation boundary of the FEM, and the final coupling matrices are obtained by using the inward-looking approach. Because the computational domain with several millions of unknowns can be solved on a personal computer, our FEM-DDM method is powerful for scattering simulation of a very large-scale rough surface with targets presence. In addition to reduction of the memory storage, the superiority of this method in computing time over the conventional FEM is also demonstrated. Our codes are examined by the FEM without DDM, the forward-backward method (FBM), and generalized FBM for some simple cases. Numerical simulations of bistatic scattering from a comprehensive model of a ship on and a target above the 2-D randomly rough perfectly conducting sea surface in large electric scale are obtained, and its functional dependence on many physical parameters of the targets and oceanic status are discussed.