SAR imaging method based on coprime sampling and nested sparse sampling

As the signal bandwidth and the number of channels increase, the synthetic aperture radar （SAR） imaging system produces huge amount of data according to the Shannon-Nyquist theorem, causing a huge burden for data transmission. This paper concerns the coprime sampl which are proposed recently but ng and nested sparse sampling, have never been applied to real world for target detection, and proposes a novel way which utilizes these new sub-Nyquist sampling structures for SAR sampling in azimuth and reconstructs the data of SAR sampling by compressive sensing （CS）. Both the simulated and real data are processed to test the algorithm, and the results indicate the way which combines these new undersampling structures and CS is able to achieve the SAR imaging effectively with much less data than regularly ways required. Finally, the influence of a little sampling jitter to SAR imaging is analyzed by theoretical analysis and experimental analysis, and then it concludes a little sampling jitter have no effect on image quality of SAR.

New slant range model and azimuth perturbation resampling based high-squint maneuvering platform SAR imaging

Strong spatial variance of the imaging parameters and serious geometric distortion of the image are induced by the acceleration and vertical velocity in a high-squint synthetic aperture radar(SAR)mounted on maneuvering platforms.In this paper,a frequency-domain imaging algorithm is proposed based on a novel slant range model and azimuth perturbation resampling.First,a novel slant range model is presented for mitigating the geometric distortion according to the equal squint angle curve on the ground surface.Second,the correction of azimuth-dependent range cell migration(RCM)is achieved by introducing a high-order time-domain perturbation function.Third,an azimuth perturbation resampling method is proposed for azimuth compression.The azimuth resampling and the time-domain perturbation are used for correcting first-order and high-order azimuthal spatial-variant components,respectively.Experimental results illustrate that the proposed algorithm can improve the focusing quality and the geometric distortion correction accuracy of the imaging scene effectively.

SAR imaging simulation for an inhomogeneous undulated lunar surface based on triangulated irregular network

Based on the statistics of the lunar cratered terrain, e.g., population, dimension and shape of craters, the terrain feature of cratered lunar surface is numerically generated. According to the inhomogeneous distribution of the lunar surface slope, the triangulated irregular network （TIN） is employed to make the digital elevation of lunar surface model. The Kirchhoff approximation of surface scattering is then applied to simulation of lunar surface scattering. The synthetic aperture radar （SAR） image for compre- hensive cratered lunar surface is numerically generated using back projection （BP） algorithm of SAR imaging. Making use of the digital elevation and Clementine UVVIS data at Apollo 15 landing site as the ground truth, an SAR image at Apollo 15 landing site is simulated. The image simulation is verified using real SAR image and echoes statistics.

SAR IMAGING SIMULATION OF HORIZONTAL FULLY TWO-DIMENSIONAL INTERNAL WAVES

Based on the research of Lynett and Liu, a new horizontal fully two-dimensional internal wave propagation model with rotation effect was deduced, which can be used to simulate the characteristics of internal waves in a horizontal fully two-dimensional plane. By combining the imaging mechanism of Synthetic Aperture Radar（SAR）, a simulation procedure was fatherly acquired, which can simulate the propagation characteristics of oceanic internal waves into SAR images. In order to evaluate the validity of the proposed simulation procedure, case studies are performed in South China Sea and results from simulation procedure are analyzed in detail. A very good consistency was found between the simulation results and satellite images. The proposed simulation procedure will be a possible foundation for the quantitative interpretation of internal waves from fully two-dimensional satellite images.

PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.

Research on synthetic aperture radar imaging technology of one-dimensional layered rough surfaces

A quick and exact imaging method for one-dimensional layered rough surfaces is proposed in this paper to study the scattering characteristics of a layered medium that exists widely in nature.The boundary integral equations of layered rough surfaces are solved by using the propagation-inside-layer expansion combined with the forward and backward spectral acceleration method（PILE＋FB-SA）,and the back scattering data are obtained.Then,a conventional synthetic aperture radar（SAR） imaging procedure called back projection method is used to generate a two-dimensional（2D） image of the layered rough surfaces.Combined with the relative dielectric permittivity of realistic soil,the random rough surfaces with Gauss spectrum are used to simulate the layered medium with rough interfaces.Since the back scattering data are computed by using the fast numerical method,this method can be used to study layered rough surfaces with any parameter,which has a great application value in the detection and remote sensing areas.

Fast segmentation approach for SAR image based on simple Markov random field

Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.

Underwater topography detection of Shuangzi Reefs with SAR images acquired in different time

Imaging mechanism of underwater topography by SAR and a underwater topography SAR detection model built on the theory of underwater topography detection with SAR image presented by Yuan Yeli are used to detect the underwater topography of Shuangzi Reefs in the Nansha Islands with three scenes of SAR images acquired in different time. Detection results of three SAR images are compared with the chart topography and the detection errors are analyzed. Underwater topography detection experiments of Shuangzi Reefs show that the detection model is practicable. The detection results indicate that SAR images acquired in different time also can be used to detect the underwater topography, and the detection results are affected by the ocean conditions in the SAR acquiring time.

PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

A detection model of underwater topography with a series of SAR images acquired at different time

underwater topography is one of oceanic features detected by Synthetic Aperture Radar.Underwater topography SAR imaging mechanism shows that tidal current is the important factor for underwater topography SAR imaging.Thus under the same wind field condition,SAR images for the same area acquired at different time include different information of the underwater topography.To utilize synchronously SAR images acquired at different time for the underwater topography SAR detection and improve the precision of detection,based on the detection model of underwater topography with single SAR image and the periodicity of tidal current,a detection model of underwater topography with a series of SAR images acquired at different time is developed by combing with tide and tidal current numerical simulation.To testify the feasibility of the presented model,Taiwan Shoal located at the south outlet of Taiwan Strait is selected as study area and three SAR images are used in the underwater topography detection.The detection results are compared with the field observation data of water depth carried out by R/V Dongfanghong 2,and the errors of the detection are compared with those of the single SAR image.All comparisons show that the detection model presented in the paper improves the precision of underwater topography SAR detection,and the presented model is feasible.

Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology

To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

Nearshore Wave Field Analysis Using SAR Images

Satellite remote sensing technique offers a wide range of information, and is one of the tools for ocean wave observation. This paper discusses the limitations of Synthetic Aperture Radar (SAR) images in wave field analysis. It is found that the wave field analysis is affected by the gray value distribution of image and the relationship between satellite travel and wave propagation directions. Since human activities and coastal engineering are performed in nearshore areas, some issues are discussed for nearshore SAR image analysis. Several case studies show that the wave parameters estimated from nearshore SAR images are quite different from in situ measurements, suggesting that the wave information derived from nearshore SAR images cannot appropriately represent the wave characteristics. One of the reasons is that the wave field is non homogeneous in the nearshore area.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

Fusion of SAR and Optical Image for Sea Ice Extraction

It is difficult to balance local details and global distribution using a single source image in marine target detection of a large scene.To solve this problem,a technique based on the fusion of optical image and synthetic aperture radar(SAR)image for the extraction of sea ice is proposed in this paper.The Band 2(B2 image of Sentinel-2(S2 in the research area is selected as optical image data.Preprocessing on the optical image,such as resampling,projection transformation and format conversion,are conducted to the S2 dataset before fusion.Imaging characteristics of the sea ice have been analyzed,and a new deep learning(DL)model,OceanTDL5,is built to detect sea ices.The fusion of the Sentinel-1(S1 and S2 images is realized by solving the optimal pixel values based on deriving Poisson Equation.The experimental results indicate that the use of a fused image improves the accuracy of sea ice detection compared with the use of a single data source.The fused image has richer spatial details and a clearer texture compared with the original optical image,and its material sense and color are more abundant.

Object Detection Research of SAR Image Using Improved Faster Region-Based Convolutional Neural Network

Target detection technology of synthetic aperture radar(SAR)imageis widely used in the field of military reconnaissance and surveillance.The traditional SAR image target detection methods need to be provided a lot of empirical knowledge because the characteristics of SAR images in different configurations(attitude,pitch angle,imaging parameters,etc.)will change greatly,resulting in high generalization error.Currently,deep learning method has achieved great success in the field of image processing.Research shows that deep learning can achieve a more intrinsic description of the data,while the model has a stronger ability of modeling and generalization.In order to solve the problem of insufficient data in SAR data sets,an experimental system for acquiring SAR image data in real scenes was built.Then the transfer learning method and the improved convolution neural network algorithm(PCA+Faster R-CNN)are applied to improve the target detection precision.Finally,experimental results demonstrate the significant effectiveness of the proposed method.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

SAR image de-noising based on texture strength and weighted nuclear norm minimization

As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization（WNNM） is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis（PCA） and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures（BLS-GSM） method, non-local means（NLM） filtering in terms of both quantitative measure and visual perception quality.

Simultaneous Speckle Reduction and SAR Image Compression Using Multiwavelet Transform

Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.

Bayesian-Based Speckle Suppression for SAR Image Using Contourlet Transform

A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is proposed.First,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional resolutions.Then,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle coefficients.Experimental results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.