The synthetic aperture radar (SAR) can be affected by various types of jamming during operation. Among them, the deceptive jamming generated by digital radio frequency memory (DRFM) jammers poses a serious threat to SAR imaging by creating highly realistic false targets. Moreover, with advancements in deceptive jamming technology, the generation speed of deceptive jamming has increased, rendering existing methods less effective. To address this issue, an anti-deceptive jamming method based on pulse repetition interval (PRI) variation design and multi-channel principle is proposed to mitigate the effects of deceptive jamming. First, a PRI variation strategy that will not cause the loss of echo signals in the imaging area is designed. By utilizing this strategy for imaging, deceptive jamming signals are dispersed across different ranges, resulting in preliminary suppression of the jamming. Subsequently, after azimuth non-uniform sampling reconstruction and range processing, most of the jamming signals are suppressed due to the azimuth timing differences between SAR and jamming signals. However, when the jammer uses specific retransmission intervals, such as the average PRI of the PRI sequence, the jamming signals may be concentrated at certain ranges, retaining some coherence and posing a threat to SAR imaging. To overcome this challenge, a residual jamming detection and suppression algorithm based on multi-channel principle is proposed, which can detect and filter out the channels affected by jamming. Finally, an azimuth sparse reconstruction is introduced for azimuth processing. Since the anti-jamming principle of this method relies on the differences in azimuth timing between SAR and jamming, it can suppress deceptive jamming even when the generation speed of deceptive jamming is rapid, which some other anti-deceptive jamming methods cannot achieve. Simulations of SAR imaging under deceptive jamming conditions are conducted for point target scene and complex targets scene. The simulation results show that the proposed anti-deceptive jamming method can effectively suppress deceptive jamming and enable high-quality imaging.

With the expansion of the surveillance area and resolution of spaceborne synthetic aperture radar (SAR) systems, the increasing amount of echo data requires further research on efficient imaging methods. The pulse repetition frequency (PRF) of traditional SAR needs to satisfy the Shannon–Nyquist sampling theory, while the PRF limits the system swath width, making it impossible to achieve wider illumination coverage. The reduction of the PRF can effectively increase the swath width, but it will cause severe azimuth ambiguity, decreasing the image quality. Several methods have been introduced for ambiguity suppression, but many of them become ineffective at lower PRFs. In this article, we propose a novel sparse imaging method for the spaceborne PRF-reduced sliding spotlight SAR. The proposed method considers the azimuth ambiguity term in the joint sparse imaging model, separately constrains the main imaging and azimuth ambiguity areas, and achieves azimuth ambiguity suppression by using compressive sensing (CS) technology. With its help, we can reduce the original PRF by up to half to double the swath width and enable high-precision sparse reconstruction of large-scale scenes. Compared with$L_{2,1}$-norm regularization-based algorithm, the proposed method shows the superior ambiguity suppression ability with less computational cost from PRF-reduced echo data. Experimental results on simulated raw data validate the proposed method.

Airborne synthetic aperture radar (SAR) systems are commonly susceptible to trajectory deviations, resulting in distinct azimuth phase error in the collected echo. SAR autofocus technology can compensate phase error and produce a well-focused image based on echo data. However, due to the influence of unfavorable factors such as radar interruption and electromagnetic interference, the echo data may be down-sampled in azimuth, which reduces the phase error estimation accuracy of traditional autofocus methods. By introducing compressed sensing (CS) to SAR data processing, sparse SAR imaging shows outstanding performance in acquiring high-resolution images utilizing down-sampled echo. However, the phase error existing in azimuth direction will reduce the sparsity of observed scene, and the precision of sparse reconstruction is consequently decreased. This paper aims at enhancing the Doppler rate error estimation precision when echo is down-sampled in azimuth and proposes a novel sparse imaging method combined with Doppler rate estimation. During each iteration of complex approximated message passing (CAMP) algorithm, the Doppler rate error is estimated according to the non-sparse solution by fractional Fourier transform (FrFT). Then phase error is compensated to the non-sparse solution, and the azimuth matched filtering (MF) operator is upgraded. The aforementioned steps are performed iteratively until a well-focused sparse SAR image is generated. It should be noted that the Armijo rule and random sample consensus algorithm (RANSAC) are introduced to guarantee the fast and precise reconstruction of the observed scene. Experiments from simulated and airborne data prove the enhancement in Doppler rate estimation precision by the proposed method than traditional estimator when used data is azimuth down-sampled.

Polarimetric synthetic aperture radar (PolSAR) can obtain rich information of ground objects through different polarization combinations, and is widely used in terrain classification. The time-varying feature of multi-temporal polarimetric SAR data is a useful supplement, which contains information that is not available in single polarimetric data. This paper aims to introduce time-varying analysis into Sentinel-1 data, so as to effectively combine the information of both time and polarization dimensions. In this way, the accuracy of crop classification using dual-polarimetric data is improved. This paper used Sentinel-1 data, firstly we constructed the dual-polarimetric coherence (DC) based on C2matrices, and analyzed the DC to find optimal time by using feature importance ranking in Random Forest model. Then, the polarimetric features and DC of the selected time are combined. Finally, the spatial correlation of MRF was utilized to classify. Compared to using only polarimetric features alone, the overall accuracy is improved.

Synthetic aperture radar tomography (TomoSAR) is a common measurement technique for three-dimensional (3-D) imaging of forested areas. It extends the synthetic aperture principle into the elevation direction, obtains the elevation scattering information of the observed target, and then realizes the 3-D reconstruction. In order to achieve high-resolution imaging, compressed sensing (CS) technology is widely used in TomoSAR. Given that many existing forested area datasets comprise multi-polarization modes, leveraging the complete potential of polarimetric data to achieve high-resolution 3-D recovery of the observed scene is essential. Therefore, this paper proposes a novel L2,1/2 -norm regularization based TomoSAR imaging method of the forested area, which jointly reconstructs the multi-polarization data and utilizes the correlation between each polarization to obtain more accurate 3-D reconstruction results. Experimental results based on real BioSAR 2008 L-band dataset are used to verify the proposed method.

Qarhan Salt Lake is located in the Qaidam Basin in northwest China, containing abundant salt mineral resources such as sodium, potassium, and magnesium. It is the largest salt lake and one of the most important salt lake industry bases in China. The changes and development of the salt crust are of great significance for understanding the ecological environmental change of the Qarhan Salt Lake and promoting sustainable production of salt lakes. However, the polarimetric scattering features are extremely similar between different types of salt crusts basically composed of dominant surface scattering with some volume and surface scattering, so the redundancy problem between features is particularly serious. The purpose of this paper is to select 7 polarimetric features with a classification accuracy of more than 95% from statistical and textural similarity, respectively, using the similarity metrics SSFSM for different salt shell polarized features, achieving a classification accuracy similar to that of all features.

Synthetic Aperture Radar (SAR) 3D imaging for complex structural buildings is a hot research topic in the SAR imaging field. Tomographic SAR and multi-aspect SAR are two main imaging modes for SAR 3D imaging. The former one requires repeat passes and is mainly used in spaceborne SAR. The latter one, which is discussed in this paper, observes the target area from multiple aspect angles and obtains multi-aspect target scattering features. However, it still faces some challenges for 3D imaging of complex structural buildings. The main problem is that the elevation resolution is limited by the anisotropic scattering of the target. To solve this problem, we propose a new 3D imaging method that combines the high accuracy elevation inversion capability of interferometric SAR and the elevation ambiguity resolving capability of multi-aspect SAR. Real data validates the proposed method.

Three-dimensional target reconstruction from 2-D synthetic aperture radar (SAR) images can reduce the difficulty of data acquisition when compared to 3-D imaging technologies. Pixel2mesh was well designed for 3-D target reconstruction from 2-D optical images. However, when it is directly applied to small SAR datasets, overfitting is prone to occur. In this letter, we propose a lightweight Pixel2mesh for 3-D target reconstruction from a single 2-D SAR image. Based on the original Pixel2mesh, we improve two subnetworks: feature extraction and 3-D deformation. First, we lightweight the graph residual network (G-ResNet) module in the 3-D deformation subnetwork to avoid overfitting. Second, we add a decoder after the feature extraction subnetwork to reconstruct the input image and then obtain the image reconstruction loss to guide the training of the whole network. In addition, we modify the Laplace regularization term for the training of the proposed network, aiming to make the deformation more reasonable. Experiments are implemented on the Gotcha dataset, where 2-D images of seven cars are obtained by performing 2-D imaging. The 3-D labels of these cars are generated by using their CAD models downloaded from public websites. Experimental results verify that our network can achieve better 3-D reconstruction results than the original Pixel2mesh.

This paper proposes a hierarchical double Dirichlet process mixture model (DDPMM) for multilook polarimetric synthetic aperture radar (PolSAR) data unsupervised classification. Specifically, within the framework of product model (PM), an observed PolSAR data point can be factorized as the multiplication representation of a positive-scalar texture variable and a complex-Wishart-distributed speckle component. Based on this assumption, the polarization DPMM and texture DPMM in the proposed model are hierarchically established to characterize the polarimetric matrix and texture variable, respectively, thus yielding the generation procedure of the observation data to be learned sufficiently. Meanwhile, instead of sharing the same texture vector in many existing PM-based methods, each data point in DDPMM is associated with its own texture vector, which can be characterized as the weighted summation of several densities via texture DPMM rather than following a single distribution, such that the texture information can be fully and flexibly captured. In particular, dual local spatial constraints based on the statistical representations of polarization and texture spaces are also explored on the two DPMMs, allowing the local correlation to be adequately and dynamically incorporated. Moreover, all closed-form updates are derived with the variational Bayesian inference algorithm and the cluster number of the proposed model can be determined automatically. Experimental results on four real PolSAR datasets demonstrate the superiority of the proposed DDPMM to some state-of-the-art methods.

Multiaspect synthetic aperture radar (SAR) observations of complex targets often exhibits distinct anisotropic feature, which also contains rich information about target structural complexity. In practical scenarios, SAR images at certain aspects may be unsampled or may not be available, in such case, interpolation of SAR images from other sampled or available aspects, known as multiaspect interpolation, is needed. This article conducts a preliminary study on the quantitative description of anisotropic scattering variation (ASV) and the relationship between ASV and multiaspect interpolation based on typical aircraft target airborne multiaspect SAR dataset. The aim is to clarify when it is more meaningful to perform multiaspect interpolation and propose ASV operator to describe the variation of anisotropic scattering, which, to some extent, can also express target structural complexity. In the exploration process, it was found that certain expression of anisotropic feature can reflect the structural and textural feature of targets, which can represent changes of anisotropic scattering. Therefore, these expressions are incorporated into ASV operator, which makes the operator effective. Experimental results show that the proposed ASV operator can accurately describe changes of anisotropic scattering based on aircraft target data. Moreover, it can predict the effectiveness of interpolated SAR images based on the value of ASV operator. Consequently, multiaspect interpolation of SAR images at required aspects can be performed based on prior predictions using sampled SAR images.

The multi-temporal polarimetric SAR data provides the difference of scattering characteristics in time dimension for terrain classification, hence it could reflect the time-variant characteristics of the same scene. Based on this advantage, crop classification is one of the important applications of multi-temporal polarimetric SAR data. However, the features of time and polarization dimension used for classification basically are from the data at each certain time, which lack the interpretation of the variant characteristics between multi-temporal data. To solve the problem, based on the specific data representation models for multi-temporal polarimetric SAR data, this paper extracts new time variant scattering features, including the change type as well as the change direction (increase or decrease). Time series Radarsat-2 data is applied for scattering change interpretation. By using the proposed features to classify crops, it is proved that the method can effectively improve the classification accuracy, and the classification performance of difference data representation models is compared.

Spaceborne spotlight SAR mode has advantages of relative wide coverage and high resolution. Moving target imaging using spaceborne SAR system is important in both civil and military applications. Current researches focus on ground and maritime target like vehicle and large ship, the topic about aeroplane is rear. Therefore, based on Chinese GF-3 spotlight SAR SLC data, a new moving aeroplane imaging method is proposed. the moving target signal model in spotlight SAR SLC is deduced, the residual range migration and azimuth quadratic phase are then analyzed. it turns out that the residual range cell migration and quadratic phase relate to the relative speed. then, the moving aeroplane can be focused iteratively via tuning the relative speed parameter. The GF-3 spotlight SAR data of a non-cooperative aeroplane is used to validate proposed method.

The traditional CNN-based methods usually employ the spatial information in the amplitude of complex Synthetic Aperture Radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multi-scale sub-band feature extraction (MSFE) component is designed for natural objects. Since we observe their multi-scale sub-band spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1.

In synthetic aperture radar automatic target recognition (SAR-ATR), the limitations of imaging environment and observation conditions make it challenging to acquire a substantial amount of high-value targets, resulting in a severe shortage of datasets. This scarcity leads to poor performance and instability in few-shot SAR target recognition. To address these shortcomings, this paper proposes Mada-SGD, a novel inner-loop parameter update approach based on meta adaptive hyper-parameter learning. By considering the correlation information between multiple update steps, Mada-SGD learns the weight distribution information of initialization parameters across previous and current update steps, akin to a memory mechanism. This approach enhances feature extraction and representation ability for few-shot SAR targets. Additionally, an adaptive hyper-parameter update strategy is introduced to simultaneously learn the initialization, weight factor, update factor, and update direction in the meta-learner. This effectively resolves parameter updating issues in meta-learning models while improving fast adaptation for few-shot SAR targets. Experimental results on the specialized MSTAR-FSL dataset demonstrate that Mada-SGD outperforms the latest few-shot SAR target recognition model in terms of SAR target recognition performance, validating its advancement and superiority.

In this article, we present a novel synthetic aperture radar (SAR) 3-D imaging strategy using circular Interferometric SAR (InSAR) data. Our approach improves upon the Holographic SAR tomography (HoloSAR) techniques by eliminating the need for multi-baseline data collection nor residual motion error correction over a long curvilinear aperture. This may provide a simple yet effective 3-D imaging solution for perturbed airborne radar platforms. The key innovation is the first utilization of multi-aspect SAR interferograms to invert the 3-D or 4-D (3-D spatial coordinates (x, y, z) and radar azimuth angles θ) scattering power distribution of the imaged scene. Our fundamental assumption is that the imaged scene conforms approximately to a random volume scattering model. Thus, we can use the projection-slice theorem to establish a mathematical relationship between the multi-look interferograms and the scene’s 3-D/4-D scattering power distribution. The fundamental concepts and resolution theory of this new 3-D inversion strategy are developed in 3-D K-space using Fourier aperture synthesis theories. Then, we designed two algorithms for reconstructing a 3-D image: a filtered back-projection algorithm for reconstructing isotropic targets, and a compressed sensing imaging method for reconstructing large-scale targets with anisotropic behaviors. Finally, we verified the feasibility of proposed methods through experiments in real airborne scenarios.

The existence of Radio Frequency (RF) Interference will cause an adverse effect on the interpretation of Synthetic Aperture Radar (SAR) images. There are various types of interference, and their pattens in images vary in different situations. Previous algorithms have disadvantages of low precision and large amount of computation. In this paper, we propose a prior-induced deep neural network. Based on the sparse and low-rank properties of interference signals in the time-frequency domain, an interference suppression network is designed to reconstruct useful signals. At the same time, a new loss function is designed, which integrates the sparse and low-rank properties with the training of network. The network combines the idea of semi-parametric interference suppression and the deep learning method, which can make good use of the characteristics of SAR echoes, making it more suitable for the field of signal processing and having a better effect. The proposed algorithm is applied to real SAR data with interference to validate its effect and efficiency.

Multi-aspect SAR can obtain more backscatter information about the target by observing targets from different azimuths by means of radar. More and more attention has been paid to the analysis of typical target characteristics in SAR scenes. The target characteristics mainly include the anisotropy and isotropy characteristics of the target. In the process of multi-aspect analysis, SAR images from different angles are routinely used to analyze typical targets in the area. In this article, the SAR images of different angles are filled into the matrix for characteristic analysis. Secondly, the full-angle data is a 360-degree coherent image of the scene. For the established matrix, we randomly remove some data to reverse the target characteristics. Calculate the feature matrix under constraint conditions by combining the matrix with the collaborative filtering algorithm, and finally get the predicted value of the missing angle. C-band circular SAR data is used to validate our method.

Radio frequency electromagnetic interference is a relatively common phenomenon, especially for synthetic aperture radar (SAR) systems working in P- or L-band. Compared with the suppression of narrowband interference, that of wideband interference, particularly of those whose signal parameters have frequently changing property, is still a sophisticated problem. In this letter, a suppression algorithm for interference with wideband and complicated parameters is proposed, based on low-rank and sparse matrix decomposition (LRSMD) in time–frequency domain (TFD) of the signal. The proposed algorithm begins with transforming the SAR signal into TFD. After that, LRSMD based on bilateral random projection (BRP) is applied to decompose the time–frequency spectrum matrix into three parts, a low-rank matrix standing for interference, a sparse matrix standing for SAR signal, and a noise matrix. Finally, inversely transform the sparse matrix into the time domain to obtain SAR signal without interference. The proposed algorithm is applied to a single look complex (SLC) SAR data of Sentinel-1 to validate its effect and efficiency.

Over the past decades, radar high-resolution range profile (HRRP) has been one of the research highlights in the field of radar automatic target recognition (RATR) due to its advantages of easy acquisition, small amount of data, and rich target structure information. However, most of existing methods only consider its amplitude (time domain) characteristics, thereby neglecting the temporal dependence and multi-domain features inside the HRRP sequence. To this end, we propose an end-to-end multi-input convolutional gated recurrent unit neural network, called MIConvGRU, for RATR by both exploiting the multi-domain and temporal information to improve the recognition performance of HRRP target. Initially, the data-preprocessing module is employed to extract the multi-domain features of the target, including time domain, frequency domain, and time-frequency domain features, in order to further enhance the target representation. In addition, a cascaded multi-input GRU structure is designed to acquire the multi-domain temporal dependence feature of HRRP sequence from low to high level. Finally, these temporal features are adaptively fused by a parameter learnable strategy. The experimental results show that the proposed MIConvGRU can effectively learn the multi-domain temporal dependence correlation features in HRRP sequences, improving the target recognition performance.

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

In synthetic aperture radar automatic target recognition (SAR-ATR), target information is usually propagated and reserved in complex-valued form, namely magnitude information and phase information. However, most of the existing SAR target recognition methods only focus on real-valued (magnitude information) calculations and ignore the phase information of targets, yielding poor recognition performance. To overcome this limitation, this paper proposes a multi-stream feature fusion SAR target recognition method based on complex-valued operations, called MS-CVNets, to utilize the phase information of the target effectively. First of all, a series of complex-valued operation blocks are constructed to satisfy the network training in the complex field, such as complex convolution, complex batch normalization, complex activation, complex pooling, complex full connection, etc. Besides, a multi-stream structure is employed by applying different convolution kernels to extract multi-scale information of targets, further enhancing the representation ability of the model. Experimental results on the MSTAR dataset illustrate that, compared with current state-of-the-art real-valued based models, MS-CVNets can achieve better recognition results under both standard operating conditions (SOC) and extended operating conditions (EOC), validating the effectiveness and superiority of the proposed method.

In the companion paper (Zhanget al., 2020), we identified two types of calibration models (CMs) that have been widely used in polarimetric calibration algorithms with distributed targets. An optimal method based on the covariance matching estimation technique (COMET), which we refer to as theOmethod, was used therein but without proof of its optimality. This article supplies the details on parameter estimation using theOmethod and proves that it is optimal in the mean-squared sense: No other estimate has a smaller mean-squared error. For data affected by the Faraday rotation (FR), the feasibility of theOmethod is analyzed. Numerical experiments are presented to compare theOmethod with existing methods and prove its optimality. The differences between theOmethod and another COMET-based calibration method are discussed. Moreover, we prove that estimating the distortion parameters while preserving the orientation angle is impossible, contrary to what is found by Ainsworthet al.(2006).

In recent years, multi-angular SAR is widely researched, including wide angle SAR and circular SAR. These kinds of SAR working modes can detect the aspect dependent scattering characteristic of the target. However, most of the researches are concerned about the amplitude information. The multi-angular phase characteristic is also an important and useful information that can be obtained from the raw data or the image. In this paper, the multi-angular phase characteristic of canonical structures is analyzed by electromagnetic simulation. Then a man-made target detection method is proposed based on the multi-angular phase characteristic. The method is validated by an X-band SAR chamber data and the GOTCHA X-band circualr SAR data. The result preliminarily shows the ability of phase on analyzing the anisotropic scattering.

Supraglacial lakes have a great impact on the mass balance and dynamics of Greenland ice sheet. While the current studies on these lakes mainly utilize optical observation data, the validity is poor, and it is impossible to conduct spatio-temporal analyses. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) data. By processing Sentinel-1 SAR dual-polarized imagery, a train dataset of 2664 image patches are formed. We use U-Net for segmentation and the associated Dice coefficient could reach higher than 95%. Besides, the terrain shadow is removed by DEM. The results are validated by supraglacial lake detection using Landsat 8. We will integrate more training data and optimize the feasibility of the model in future work.

Synthetic aperture radar (SAR) is widely used in many applications due to its all weather and all day observation capabilities. With the development of many spaceborne and airborne SAR systems, the amount of SAR data and products is increasing rapidly. Meanwhile, how to ensure the quality of data and products becomes more important. Calibration and validation are the essential processes to improve and evaluate the quality of the data and products. Standardization of SAR calibration and validation is a critical step towards higher product quality and better comparability and interoperability. This paper presents recent efforts and future work of SAR calibration and validation of ISO/TC 211.

Synthetic aperture radar tomography (TomoSAR) obtains three-dimensional reflectivity of scenes by extending the aperture principle into the elevation direction, and thus solves the layover problem. Because the scatters are sparse along elevation, compressed sensing methods are introduced into TomoSAR. However, conventional compressed sensing methods based on discretized grids suffer from off-grid effect. As a method with continuous dictionary, reweighted atomic norm minimization (RANM) can avoid this problem completely. In this paper, we propose an algorithm for SAR tomographic imaging based on reweighted atomic norm minimization to eliminate off-grid effect and locate scatters more accurately. The performance of the proposed method is verified by simulated data.

Multi-angular SAR can be used to obtain the information of target scattering characteristics at different aspect angles. The scattering anisotropy extraction attracts more attention but the method is not much. And because of background noise, the anisotropy extraction based on aspect entropy is not good. Low-rank matrix decomposition is widely used in the change detection process of SAR images. The most important thing is that it can distinguish the strong point target from the background image and the sparse matrix obtained by decomposition eliminates the sidelobe noise of the target. In this paper, firstly we proposed the application of low-rank matrix decomposition to multi-angular SAR images to analyze the scattering characteristics and then the coefficient of variation is used to validate and quantify the anisotropy of the target in the scene after low-rank matrix decomposition. The anisotropy quantization result is less affected by noise than aspect entropy. The Gotcha X-band circular SAR data is used to validate the method.1

Reconstructing a three-dimensional (3-D) target model from a collection of multi-aspect SAR images has been a hotspot. When imaged from different viewing aspect angles, a 3-D target will project onto different 2-D locations and present different geometric shapes. Echoes from the targets generate the non-background areas in the SAR imagery, so the geometry of the background areas can be used to restrict the possible 3-D shapes and positions of the targets. In this paper, we develop a background constraint for checking the consistency between 3-D models and the subaperture image sequence. Then a 3-D reconstruction algorithm using single-pass circular SAR (CSAR) imagery is proposed. The algorithm iteratively removes the incompatible voxels from an initial 3-D model by checking the constraint-consistency of each illuminated voxel in the model until the model converges. Also, we use a ray tracing strategy to check whether a voxel can be illuminated by the radar, so the proposed algorithm can robustly deal with the shadow effects in SAR images. The performance of the algorithm is validated using the C-band CSAR imagery acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS).

Spotlight synthetic aperture radar (SAR) achieves very high-resolution (VHR) images by steering the azimuth beam during the formation of the synthetic aperture. In practice, the steering is implemented through discrete azimuth beam switching. Then, phase shifts can occur between the adjacent beams due to the error of the antenna pattern. In addition, the troposphere introduces a beam-angle-dependent delay to the echo. Those undesired phase shifts and delays cause phase errors in the received echo and result in image quality deterioration. In this letter, an algorithm, called the Newton entropy minimization (N-EM), is proposed to estimate and compensate the phase errors caused by the discrete azimuth beam steering for the spotlight SAR data. Combining with the subaperture imaging approach of the spotlight SAR, the algorithm estimates the phase offsets between each couple of the adjacent beams based on the minimum entropy criterion. The analytic expression, which is a nonlinear equation, is developed for the optimal estimation. Then, the Newton's method is employed to solve the equation. The real spaceborne SAR (both the staring and sliding spotlight modes) data processing results demonstrate the efficiency and accuracy of the proposed algorithm.

Polarimetric synthetic aperture radar (PolSAR) image classification is an important part of SAR data interpretation and provides more intuitive and detailed SAR polarization information. To bridge the PolSAR data and applications, it is necessary to design a comprehensive PolSAR classification framework to achieve satisfactory results. The deep neural network (DNN) appears to be a solution for the classification issue, in which it outperforms the classical supervised classifiers under the condition of sufficient training data. However, the volume of training data will greatly limit the effectiveness of practical applications. In this article, we try to solve the dependence issue on training data in three different ways: recurrent learning, data augmentation, and postprocessing. First, the long short-term memory (LSTM) network is introduced to achieve pixel sequence learning by taking into account the spatial and polarimetric features. Second, the random neighbor pixel-block (RNPB) method is proposed to increase the number of training samples for sequence learning. Third, the conditional random field (CRF) model is employed to further improve the classification accuracy. In the experiments, three sets of PolSAR data are used to evaluate the small sample performance of the proposed classification method. With only 0.5% labeled pixels for training, the proposed RNPB-LSTM-CRF method can approach 99% overall classification accuracy for all the data sets. Compared with the existing methods, the proposed method can achieve state-of-the-art results for PolSAR image classification under the condition of 1% training samples.

To date, several different methods for polarimetric synthetic aperture radar (SAR) calibration with distributed targets have been proposed in the literature. The basic assumptions on the distributed target that are used by these methods are almost identical. Their difference is about the assumptions on noise. In this article, the research shows that the subtle difference between the assumptions on noise leads to two different calibration models (CMs), which is the primary cause of the differences between various methods. According to the used CM, the methods in the literature can be categorized into two groups. Because this article focuses on the CMs, thus the optimal estimator in each group is used for comparison so as to exclude the impacts of different parameter estimation algorithms. The results suggest that neither of the optimal estimators is always superior to the other. In practice, we cannot determine which estimator is better, so we recommend using the mean value of the two optimal estimators (i.e.,$ { \widehat {\boldsymbol \varphi }}^{\star } $) for calibration because it was proved to be (at least) better than the worse one. In the research, the signal-to-noise ratio (SNR) was proved to be a proper indicator for assessing whether${ \widehat {\boldsymbol \varphi }}^{\star } $is reliable. Hence, an estimator for the mean SNR is proposed. In this article, some simulation experiments are used to verify some critical conclusions that we have drawn. The practical use of${ \widehat {\boldsymbol \varphi }}^{\star } $and an assessment of its reliability with the estimated mean SNR are illustrated with DLR E-SAR data from the 2006 AgriSAR campaign.

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

Complex-valued convolutional neural network (CV-CNN) has been presented in recent years. In this letter, CV full convolutional neural network (CV-FCNN) is proposed for synthetic aperture radar (SAR) target classification, which contains only convolution layers in the hidden layer. The purpose of replacing both the pooling and fully connected layers in CV-CNN with the convolution layers is to avoid complex pooling operation and prevent overfitting, respectively. Considering the label of target is always real-valued, the magnitude of the complex vector obtained from the last convolution layer is calculated before softmax classification in the output layer. Moreover, the back-propagation formula for each layer of CV-FCNN is presented in detail. Furthermore, the complex$1\times 1$convolution layer is added into CV-FCNN to learn the cross-channel information of feature maps. The experimental results show that the average accuracy can be improved using CV-FCNN, and it is further improved using CV-FCNN with the$1\times 1$convolution layer.

In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method.

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method.

Circular Synthetic Aperture Radar(CSAR) has become a hotspot with its characteristic of elevation plane resolution and all-aspect observing ability. Digital elevation model (DEM) extraction in urban arears by using single-pass CSAR data without requiring additional knowledge is a subject of interest. The target, whose real height is not equal to the reference imaging height will project to different locations after imaging in different sub-aperture. In this paper, the quantitative relationship between offset of imaging points and height difference is deduced theoretically in the real scene, where the airborne SAR platform trajectory is not a standard circle. DEM of an area is presented using the data acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS). Compared with the DEM provided by the German Aerospace Center (DLR) with 1m absolute height error, the effectiveness of the proposed method is verified.

Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets' intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.

Circular synthetic aperture radar (CSAR) can provide distinctive multi-aspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in a SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multi-aspect and fully polarimetric SAR signatures of point-like and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by use of constant false alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science (IECAS). The results indicate the framework can retain anisotropic scattering and extract a series of new multiaspect polarimetric SAR signatures for terrain classification.

The polarimetric features of PolSAR images includes the inherent scattering mechanisms of terrain types, which is important for classification and other earth observation applications. By the use of target decomposition methods, many polarimetric scattering components can be obtained. Besides, the elements of Coherency/Covariance Matrix, as well as polarimetric descriptors such as SPAN, SERD/DERD etc., can also provide characteristic information. However, the computation cost will be very high if all of the polarimetric features are employed as the input of the classification process. In this paper, the effective polarimetric feature combination are studied based on the classification performance of SVM (Support Vector Machine) and NRS (Nearest-Regularized Subspace) machine learning approaches. A fast strategy on basis of correlation coefficient is used to select the features for classification experiments. For the airborne PolSAR data in Flevoland, 10 features have been selected from the total 107 polarimetric features with good classification accuracy up to 93.6%. The experiments on other data sets will be shown.

Circular synthetic aperture radar (CSAR) can provide distinctive multiaspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in an SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multiaspect and fully polarimetric SAR signatures of pointlike and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by the use of constant false-alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science. The results indicate that the framework can retain anisotropic scattering and extract a series of new multiaspect polarimetric SAR signatures for terrain classification.

Using sparse signal processing to replace matched filtering (MF) in synthetic aperture radar (SAR) imaging has shown significant potential to improve image quality. Due to the huge computational cost needed, it is difficult to apply conventional observation-matrix-based sparse SAR imaging method for large-scene reconstruction. The azimuth-range decouple method is able to minimize the computational complexity and achieve image performance similar to that obtained by the observation-matrix-based algorithm. However, there still exist two difficult problems in sparse SAR imaging, i.e., real-time processing and lack of raw data. To solve these problems, this paper presents a novel complex-image-based sparse SAR imaging method. It is found that if the input MF-recovered SAR complex image is obtained via fully sampled raw data, the proposed method can achieve an identical high-resolution image to that obtained by the azimuth-range decouple algorithm. The computational complexity is also decreased to the same order as that of MF, which makes the real-time sparse SAR imaging become possible. In addition, it should be noted that even though without raw data, the proposed method can still obtain impressive sparse recovery performance by using only the available complex image. Performance analysis and experimental results on real data validate the proposed method.

Polarimetric synthetic aperture radar (PolSAR) features have great significance in application of vegetation classification, which can explain the scattering mechanism of the vegetation; in order to make full use of PolSAR features' scattering mechanism explanation, the decision tree classifier is chosen because of its simple and hierarchical classifier structure. Since all the classification methods are composed of two parts: feature selection and classifier selection, this method is established with PolSAR features as selected feature and decision tree as adopted classifier. As decision tree classifier is flexible in discriminant rules, the hierarchical classification process of multi-feature is built under the notion of Fisher Linear Discriminant (FLD); after the classification process, optimization of the branch sequence and boundary algorithms is made to improve the classification accuracy of the specific classes. The experiments of AIRSAR and AgriSAR data illustrate that this method can obtain good classification accuracy; at the same time, it can introduce expert knowledge into the whole framework to help improve the classification accuracy, and extract useful information of features and classifiers from the classification results as new expert knowledge.

A new ground moving target indication algorithm for single channel Circular Synthetic Aperture Radar (CSAR) is presented and evaluated by airborne CSAR dataset. The algorithm is based on overlap subaperture magnitude images. The subaperture image can be regarded as the background image (clutter) plus the foreground image (moving target). The background image is obtained by median filter. Then the moving targets can be detected by using the subaperture magnitude images subtracting the background image. The algorithm is tested by GOTCHA GMTI dataset. The algorithm is capable of real-time processing and detecting multiple moving targets. And the initial results is presented in the paper.

This paper considers techniques of aspect feature extraction and application of targets taken over one or more wide-angle apertures. Compared with traditional narrow-aperture Synthetic Aperture Radar (SAR), multi-aspect SAR can provide observation of targets in more azimuth directions. Radar backscattering is typically characterized by location and azimuth directions. Thus target anisotropic scattering properties can be exploited with multi-aspect SAR. Firstly, data inversion method is proposed to extract the Radar Cross Section (RCS) of target from each azimuth direction independently. And then, target aspect features which are target scattering persistence angle, peak value and main scattering direction are defined. These parameters' availability is validated by experimental data which is collected in chamber and airborne data. Finally, adaptive imaging method based on target scattering persistence angle is proposed to improve the existing Circular-SAR (CSAR) imaging methods. Additionally, porlarimetry is combined with target aspect features to realize the power lines extraction. Above applications of target feature extraction is achieved with airborne data.

In this letter, we propose an imaging algorithm for the holographic synthetic aperture radar tomography in the circumstance of sparse and nonuniform elevation circular passes. Considering the anisotropic behavior of scatterers and the off-grid effect of sparse signal recovery, the algorithm combines the 2-D adaptive imaging method for circular SAR and the sparse Bayesian inference-based method for elevation reconstruction. For each circular pass, the azimuth-range 2-D image can be formed by the adaptive imaging method, which depends on the preretrieved maximum azimuth response angle and the azimuth persistence width. To deal with the off-grid effect in elevation reconstruction, which is caused by the deviation between the true scatterers and the discretized imaging grids, the off-grid sparse Bayesian inference method jointly estimates the scatterers and elevation off-grid error by applying their hierarchical priors. Compared with the conventional compressive sensing method that does not concern the off-grid effect, the proposed algorithm can provide more accurate 3-D reconstruction for pointlike targets, which is verified by the real-data experiments.

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, Lq(0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional Lq-regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical Lqregularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L1regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L1regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

This paper proposes a novel azimuth-range decouple-based L1regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional L1regularization technique requires transferring the (2-D) echo data into a vector and reconstructing the scene via 2-D matrix operations leading to significantly more computational complexity, it is very difficult to apply in high-resolution and wide-swath SAR imaging, e.g., TOPS. The proposed method can achieve azimuth-range decouple by constructing an approximated observation operator to simulate the raw data, the inverse of matching filtering (MF) procedure, which makes large-scale sparse reconstruction, or called compressive sensing reconstruction of surveillance region with full- or downsampled raw data in TOPS SAR possible. Compared with MF algorithm, e.g., extended chirp scaling-baseband azimuth scaling, it shows huge potential in image performance improvement; while compared with conventional L1regularization technique, it significantly reduces the computational cost, and provides similar image features. Furthermore, this novel approach can also obtain a nonsparse estimation of considered scene retaining a similar background statistical distribution as the MF-based image, which can be used to the further application of SAR images with precondition being preserving image statistical properties, e.g., constant false alarm rate detection. Experimental results along with a performance analysis validate the proposed method.

Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction into the problem of sparse signal recovery, we introduce two kinds of gridless sparse recovery (GL-SR) methods to DLSLA 3-D SAR cross-track imaging, i.e., atomic norm minimization (ANM) and gridless SPICE (GLS). Compared with the conventional grid-based sparse recovery (GB-SR) methods, which assume that the scatterers are exactly on the discretized grids, the GL-SR methods can avoid the off-grid effect. Experiments compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction.

In this study, we investigated the unsupervised terrain classification for hybrid polarimetric (HP) SAR by using the circular polarization ratio (CPR) parameter alone. According to the theoretical deduction based on scattering matrices of ideal polarimetric scattering mechanisms (PSMs), CPR is suggested to be processed with the logarithmic function in order to have a balanced span between the ideal PSMs' CPR values, which in turn can improve the identification of real PSMs. Utilizing one simulated HP dataset, the performance of the proposed logarithmic CPR parameter is first compared with that of classical m and χ parameters, and then assessed with real PSM classes identified by the H-α classification algorithm. Finally, a simple classification scheme of terrains is proposed and validated.

Multi-aspect PolSAR data contains polarimetric properties from different look angle. Multi-aspect polarimetric information can be applied in geometric measurement, target identifying, precise classification. In order to characterize anisotropic target, anisotropic and isotropic scattering need to be separated from the raw data. A detecting-removing-incoherent-adding (DRIA) framework, presented in Li Yang's doctoral dissertation, suggests to remove the anisotropic scattering, gain a removal series and incoherent integrate the reserved data. In this paper, in order to identify anisotropic target, an anisotropic scattering model is raised. An improved detection and feature retrieval method is presented base on DRIA framework. The equivalent number of looks (ENL) used in Li Yang's dissertation is proved to bring measurement error to the result. The anisotropic scattering can be correctly identified after the error is restored. Two kinds of maximum-likelihood ratio are proved to gain the same result in sort. Three features are retrieved from the removal series to describe the anisotropic scattering. The experimental data is circular SAR (CSAR) data acquired by the Institute of Electronics airborne CSAR system at P-band.

PolSAR features have great significance in application of vegetation classification, which can explain the scattering mechanism of the vegetation; the decision tree classifier not only can obtain good classification accuracy, but also can adjust the classification results, as well as make full use of PolSAR features to explain the scattering mechanism of the targets because of its simple and hierarchical classifier structure. Since all the classification methods are composed of two parts: feature selection and classifier selection, this paper established a classification method with PolSAR features as selected feature and decision tree as adopted classifier. As decision tree classifier is flexible in discriminant rules, the expected design of the experimental scheme introduces multiple data sources, multiple features and multiple classifiers into the framework of this classification method. In addition, discussion about how to improve the classification accuracy of the specific target has been made. The experiment of AIRSAR-Flevoland data illustrates the feasibility of this method.

Desertification of semi-arid grasslands is a serious problem for economic development and ecological preservation. Using the Hunshandake Sandy Lands as an example, we present an overview of monitoring of land desertification using two different data sources including TM and MODIS data. The driving mechanisms of Hunshandake Sandy Land desertification are also discussed.

The target feature is sensitive to the aspect angle of SAR observation, making the interpretation and target recognition of the SAR image difficult. The information acquired from a certain aspect angle is partial and incomplete, and the multi-aspect observations have the potential to improve the SAR performance in this aspect. Three topics of fine feature description of multi-aspect SAR observations are discussed, and they are the 3D information extraction, the optimum imaging strategy for anisotropic scatterers, and the multi-aspect scattering feature extraction. The initial results of the real P band airborne circular SAR (CSAR) data and the turn table data show that multi-aspect SAR observations have the encouraging potential capability in target fine feature description.

GF-3 is the Chinese Synthetic Aperture Radar (SAR) satellite mission with scientific and commercial applications, which will be launched in 2016. The ortho-rectification image combined with the DEM data can only be satisfied with the applications of the high resolution radar image, the data quantity with wide-breadth also put forward higher request to the automation processing. The Reverse-Range-Doppler method was used to ortho-rectified the SAR image of GF-3 satellite based on the Range-Doppler model in this paper. It not only ensures the correction precision, but also simplifies the iteration steps about DEM data, and improves the efficiency of automatic processing.

Multi-aspect observation can obtain the target's multi-aspect scattering feature, and has potential to improve the SAR performance in the applications of target classification and recognition. This paper presents a time-division scanning mode based on Single Phase Centre Multiple Azimuth Beam (SPC-MAB) technique. By applying this new mode, Synthetic aperture radar (SAR) can obtain multi-aspect data from continuous scenarios through linear flight. We take six channels as an example, and analyzing the performance of the time-division scanning mode and two conventional SPC-MAB modes. According to noise equivalent sigma zero (NESZ) and azimuth ambiguities to signal ratio (AASR) simulation results, the time-division scanning mode shows better performance than conventional modes. The time-division scanning mode will be applied to the multi-aspect data acquisition vehicle-mounted system.

Airborne downward-looking linear-array three dimensional synthetic aperture radar (LA-3D-SAR) can achieve high-resolution three dimensional imagery with a uniform antenna array. However, the actual 3D imagery is unavoidably degraded by amplitude-phase errors due to non-ideal antenna characteristics and motion measurement deviations. This paper investigates the effects of these errors on the forms and the degrees of image quality degradation, and considers the use of corresponding calibration methods to eliminate the effects of errors. Two calibration methods are proposed, which are based on external parallel and point target calibrators, respectively. At last, real data experiments have shown the validity of the analyses as well as the effectiveness of the proposed calibration methods.

This paper focuses on the displacement Estimation and deformation monitoring experiment analysis based on Time series data. The experimental results demonstrate the validity and correctness of the methods in the paper, and the slope monitoring using ground based SAR is proved to be effective.

Compared with traditional linear Synthetic Aperture Radar (SAR), Circular-SAR(CSAR) can achieve 360-degree observation of targets. For this reason, CSAR is the best mode of SAR to exploit target feature extraction on azimuth directions. Over larger azimuth angular extents the energy reflected by targets is, in general, not uniform and most targets exhibit only limited scattering persistence. The algorithm proposed in this paper extracts the width of target persistence angle which is defined as target azimuth-angle sensitivity. It is an effective parameter to distinguish different types of anisotropic targets. And it is independent of azimuth orientations for its measurement of width of target persistence angle. This feature is extracted by data inversion in this paper. Compared with the sub-aperture approach which can also extract the anisotropic scattering properties of target, data inversion approach in our algorithm can provide more accurate results and finer curve. This target multi-angle scattering sensitivity feature extraction algorithm is validated by experimental data which is collected in chamber.

Downward-looking sparse-linear-array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track reconstruction usually suffers from incomplete observation and limited resolution. The incomplete observation is caused by the sparse and nonuniform distribution of the equivalent antenna phase centers (APCs) due to the array elements' installation location restriction, loss, or deviation. Sparse recovery methods provide a solution with improved resolution from the incomplete observation for the 3-D imaging scene that behaves with spatial sparsity. However, conventional grid-based sparse recovery (GB-SR) methods are under the assumption that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. In this letter, we propose a reweighted scheme-based gridless sparse recovery (GL-SR) method, i.e., reweighted gridless sparse iterative covariance-based estimation (RGLS), for DLSLA 3-D SAR cross-track imaging. The proposed method possesses the merits of gridless SPICE (GLS), i.e., free of off-grid effect and user parameters, and has a statistically more appealing property than GLS by adopting the reweighted scheme. As seen from the experiments that compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction, the proposed method performs outstandingly under the circumstance of sparse and nonuniform APCs' distribution.

In circular synthetic aperture radar (CSAR) mode, a 2-D image contains 3-D spatial information about the imaged targets. This letter describes an attempt to estimate the height profiles of power lines with slightly varying heights based on 2-D CSAR imagery. First, according to the characteristics of a 2-D CSAR image of a power line with constant height, an approximate formula is deduced to calculate the height of the power line. This formula can also be used to calculate the height of several power lines with the same constant height. Second, a three-step processing method is proposed for height profile estimation of power lines with slightly varying heights. The first step is to extract regions of power lines automatically. The second step is to obtain the initial heights of the power lines according to the deduced approximate formula. The third step is to estimate the final height profiles of the power lines. Finally, experimental results have shown that 2-D imaging results of power lines with slightly varying heights are well focused when the height profiles estimated by the proposed three-step method are used.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) operates downward-looking observation and obtains the 3-D microwave scattering information of the observed scene. The cross-track physical sparse linear array is often configured to obtain uniform virtual phase centers in order to adopt the frequency-domain algorithm. However, the virtual phase centers usually have to be nonuniformly and sparsely distributed due to the array elements' installation locations restricted by the airborne platform and the airborne wing tremor effect. In this state, the frequency-domain algorithm cannot be directly used. In this paper, a DLSLA 3-D SAR image reconstruction algorithm that combines polar formatting and L1regularization is presented. Wave propagation and along-track dimensional imaging are first finished after polar formatting and wavefront curvature phase error compensation; then, cross-track dimensional imaging is completed with the L1regularization technique. The proposed algorithm is applicable to airborne DLSLA 3-D SAR imaging under nonuniformly and sparsely distributed virtual phase centers condition. The proposed algorithm was verified by 3-D distributed scene simulation experiment (P-band circular SAR image was selected as radar cross-section input, and X-band digital elevation model of the same area was selected as the coordinate positions of the scene) and the field experiment. Image reconstruction results and image reconstruction performances, such as normalized radar cross section, height errors, and orthographic projection image grayscale distribution, are demonstrated and analyzed with different signal-to-noise ratios, different array sparsity, and the incomplete compensated residual oscillation error 3-D distributed scene simulation experiments. Simulation and field experimental results show the good performance in focusing and the robustness of the proposed algorithm.

In a conventional synthetic aperture radar (SAR) image, a moving target may be smeared and displaced. Taking direct action on the defocused region of interest (ROI) data from the result of a conventional imaging algorithm, this paper presents an imaging method of the ground moving target in high-resolution SAR. A 2-D equivalent velocity parameter space is built along the azimuth and range directions with the derivation of an exact analytic expression of the ROI. In each pair of equivalent velocity parameters, the Stolt interpolation is used herein to remove the residual phase error. After that, a graph of the ROI complex subimage contrast is produced with respect to the equivalent velocity parameter space. Based on the maximum contrast principle, the desired equivalent velocity is then estimated and applied for deblurring the ROI. Finally, we can achieve the refocused SAR image of the moving target. Different from the conventional approach of moving target autofocusing that requires resynthesizing back to the full data from the cropped ROI data, the proposed method directly operates on the small-sized defocused ROI subimage without any resynthesizing operations. It is helpful for the computational burden reduction, procedure simplification, and clutter interference suppression. The experiments on synthetic and real data are carried out to validate the effectiveness of the proposed method.

In this paper, an unsupervised classification for compact polarimetry SAR (C-PolSAR) image is proposed by combining the H/α decomposition with the Wishart classifier. Firstly, H/α decomposition method is applied to the compact polarimetry (CP) data. By analyzing the different (H, a) values corresponding to the three compact polarimetry mode: the π/4, CL, and CC modes, we find that only in the CC mode, different scattering targets are distinguished well by (H, α) values. The decomposition results are used as the initial classification. Then the maximum likelihood classifier based on the complex Wishart distribution is adopted to classify the image iteratively. After four iterations, the classification results are much improved, and the classification details can be identified clearly. We use the AirSAR of San Francisco L-band data to illustrate the effectiveness of the proposed classification method.

Recently, the sparse reconstruction algorithms (SRAs) based on compressive sensing (CS) have been applied in the fields of synthetic aperture radar (SAR) imaging and show plenty of potential advantages. However, due to the great computational complexity and memory cost caused by matrix-vector multiplications, most of these algorithms are not suitable to reconstruct large-scale observed scenes. To solve this problem, we construct a backprojection based imaging operator, and introduce it to the complex approximate message passing algorithm (CAMP). The new image formation algorithm is called BP-CAMP in this paper. Compared with the approximated observation methods deduced from the FFT-based imaging technology, BP-CAMP is not limited by observation models of the radar and motion modes of the platform, and it therefore possesses universal applicability. By the simulations and real data processing, the experimental results show that BP-CAMP has lower computational complexity and memory cost than CAMP, and also achieves SAR imaging with under-sampled echo data.

Microwave imaging, or synthetic aperture radar (SAR) shows its remarkable importances in various fields of remote sensing. Modern SAR system usually comes with high imaging resolution and wide mapping swath. This brings difficulties to the future development of SAR system. As a solution, the concept of SAR imaging under sparse constraint, or sparse microwave imaging radar is suggested, which is mainly the idea of introducing the sparse signal processing theory to the radar imaging. Under the sparse constraint, this technique could bring us benefits including better imaging performance e.g. lower ambiguity, higher resolution, lower side lobe and lower system complexity [1, 2, 3, 4].

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

Tomographic synthetic aperture radar imaging (TomoSAR) is a new SAR imaging modality that extends the synthetic aperture principle into the elevation direction. In TomoSAR, the elevation resolution depends on the length of elevation aperture and the baseline distribution. When the length of elevation aperture is fixed, the number of baselines is important for the elevation reconstruction. Therefore, improving the elevation imaging quality with the finite amount of baselines is worth researching. This letter proposes a novel missing data compensation approach in TomoSAR via matrix completion (MC), which is a technique of the low-rank matrix recovery from a subset of the matrix elements. In the proposed method, we first exploit MC to estimate the 2-D focused image data of unknown baselines, which are located on the uniform data grid without changing the length of elevation aperture. Based on the recovered data, we then recover the elevation reflectivity function by the spectral analysis (SA) method. Compared with the conventional SA technique, the proposed approach can achieve much higher elevation image quality, due to more available data offered by MC. The effectiveness of the proposed method is validated with the experimental results based on simulated and real airborne data.

In this letter, an accurate topographical phase is applied to the model-based (odd-bounce, double-bounce, and volume scattering) decomposition of synthetic aperture radar (SAR) interferometry data. The decomposition procedure considered here is a determined nonlinear equation system that can be solved numerically. The accurate topographical phase is first estimated and then used as the initial input parameter to our numerical method. This approach avoids large errors generated by the constant topographical phase in fluctuating forested areas. Additionally, the modified volume scattering model introduced by Yamaguchiet al.is applied to the polarimetric SAR interferometric target decomposition data of forested areas, rather than the purely random volume scattering of Freeman and Durden, to produce the best fit to the measured data. This method retrieves the magnitude associated with each mechanism and their heights. The quality of the decomposition is demonstrated using L-band simulated data created with PolSARproSim software and L-band airborne data (BioSAR 2008) acquired by the DLR E-SAR in the Vindeln Municipality in northern Sweden.

In this letter, the influence of a polarization distortion at transmission and the Faraday rotation on compact polarimetric synthetic aperture radar (C-PolSAR) measurements is assessed. Polarization distortions at transmission for a C-PolSAR system can be introduced by nonideal transmission polarization waveforms, channel imbalance, and crosstalk. In addition, the Faraday rotation can also cause a distortion in a low-frequency spaceborne SAR system. The aforementioned two kinds of distortions have different influences on the measured data and the$H/\bar{\alpha} $decomposition. This letter assesses the polarization quality of a C-PolSAR system and evaluates the various effects of errors on the compact polarimetric parameters, including polarimetric entropy$H$and$\bar{\alpha} $. Quality assessment and error analysis can be used to inform the C-PolSAR system design. Canadian AirSAR L-band data are used to assess the impact of the distortions on$H/\bar{\alpha}$.

Compressed sensing (CS) techniques can reduce the sampling rates required in synthetic aperture radar (SAR). However, it is difficult to use the restricted isometry property to theoretically analyze the performance. Therefore, in this letter, information theory is applied to set necessary bounds on sampling ratios in CS-based SAR imaging. The system is viewed as a multi-input/multi-output (MIMO) channel, with information capacity quantified for a given measurement matrix and signal-to-noise ratio (SNR). According to the source-channel coding theorem, the lower bound of the sampling ratios is derived in terms of sparsity ratio, SNR, bandwidth, and radar pulse duration. Simulation studies are performed to test and analyze the information-theoretical bounds.

In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input-multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

In this letter, a novel 3-D imaging algorithm for down-looking linear-array synthetic aperture radar is presented. The usual algorithms involve either time-consuming interpolations or huge oversampling operations, which adds huge burden to the image formation processor. The proposed algorithm, in contrast, is not only accurate because little approximation has been made but also quite efficient because of two major endeavors that we have made: First, a novel 3-D pseudopolar domain is introduced for the focused data to avoid any oversampling and to make it possible to formulate the image formation process as an interpolation-free image series expansion, and second, convergence acceleration theory in computational mathematics is introduced to accelerate the convergence of the image series expansion. The 3-D imaging capability is evaluated, and the validation of the proposed algorithm is done by exploiting simulated data.

At present, many polarimetric synthetic aperture radar (POLSAR) remote sensing applications still perform radio metric terrain correction, whereas other types of terrain slope analysis rely on Shuttle Radar Topography Mission and Advanced Spaceborne Thermal Emission and Reflection Radiometer digital elevation model (DEM) databases. However, the terrain relief characteristics present in fully POLSAR images can also be used for topography retrieval from single-flight data. Based on the analysis of the polarization signature peak shift, we propose a new topography retrieval algorithm based on the polarization-dependent intensity ratio. The intensity term conforms to both the polarimetric orientation angle shift model and the Lambertian scattering model, which is refined for synthetic aperture radar (SAR) imaging geometry. This is useful in avoiding solving an ill-conditioned problem, and it is more practical than considering the two models independently. Another key idea of this algorithm is the derivation of a second-order residual error equation for revising the ground-range slope error with homogeneous media using the full multigrid (FMG) technique. Furthermore, the “fake” topographic relief effect induced by geophysical terrain variations is also considered and reduced by including an $\bar{\alpha} $ scattering angle-based weighting coefficient in the second-order residual error equation. In addition, a first-order residual error equation combined with tie points is proposed for calculating the least squares estimation of the height from slope integration by FMG. National Aeronautics and Space Administration Jet Propulsion Laboratory AIRSAR and UAVSAR L-band POLSAR and interferometry SAR DEM data are used to illustrate and validate this algorithm. Our results demonstrate the high potential of this method for employing a low- resolution DEM to match the corresponding high-resolution POLSAR data.

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.

In this paper we apply PolInSAR decomposition techniques to compact-pol SAR Interferometry. The complex cross-correlation matrix of single-baseline polarimetric SAR Interferometry is decomposed into three 2 × 2 scattering matrices corresponding to surface scattering, double scattering and random volume scattering. A numerical method is applied to solve the system of nonlinear equations involved in the decomposition procedure. According to the above decomposition procedure, the power contribution and vertical displacement may be obtained for each of the three scattering mechanisms, from compact-PolInSAR data. Finally we compare the compact-PolInSAR target decomposition results with those of quad-PolInSAR, to assess the ability and accuracy of compact-PolInSAR target decomposition.

HJ-1C satellite was launched in November 2012 as the first civil spaceborne SAR system of S band in China. Since the image geolocation is essential for the quantificational study of the system specifications of HJ-1C, the paper focuses on the geolocation of HJ-1C SAR image, including the systematic position accuracy of HJ-1C satellite image, and the higher position accuracy with GCPs(ground control point). In addition, a novel equivalent-range-doppler method is presented in this paper to geolocate HJ-1C satellite image using only one GCP.

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar operates nadir observation and obtains the 3-D microwave scatter information of the observed scene. A polar format algorithm (PFA) with space-variant wave-front curvature phase error compensation is presented. A 3-D image in polar coordinate can be obtained with the proposed PFA, and a 3-D image in Cartesian coordinate can be obtained with interpolation. The proposed PFA possesses the advantages of high precision, low memory requirement, and low computational complexity. The focus performance of the proposed PFA is validated by 3-D distributed scene simulation with an airborne X-band digital elevation model and a P-band circular SAR image of the same area as simulation scene input.

Paired echoes are the typical manifestation of Doppler characteristics caused by vibrating targets in high-resolution synthetic aperture radar (SAR). Conventional imaging algorithms produce smeared paired echoes. It results in not only the inconvenience of vibration parameter analysis but also the emergence of unwanted ghost targets to degrade SAR image quality. This paper proposes a method on paired echo focusing and suppression of vibrating targets. After demodulation and range compression, the signal is decomposed into the form of Bessel series in the azimuth direction. Then, the range walk is compensated in the 2-D frequency domain by Doppler keystone transform. Next, the range curvature is corrected in the range-Doppler domain by using range cell migration correction to achieve the focus of paired echoes. Compared with conventional SAR imaging algorithms, the focused paired echoes could reach system nominal resolution. Furthermore, this focusing method, which works without prior knowledge of vibrating targets, is suitable for various vibrating states of multiple targets. On the basis of paired echo focusing, the residual video phase of paired echoes is also eliminated. Then, vibration parameters, including vibration frequency, amplitude, and initial phase, are estimated. These parameters are used herein to construct the reference function to compensate the sinusoidal modulation phase in the range-Doppler domain. Finally, deghosted vibrating targets can be obtained. Simulations show that paired echoes could be successfully focused within one resolution unit as well as the robustness of its application. At last, the real SAR data from a moving truck are used to further validate the effectiveness of the proposed method.

In this paper, we introduce a total variation (TV) regularization model for SAR image despeckling. A dual formulation based adaptive total variation (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV regularization based image restoration model has a good performance in preserving image sharpness and edges while removing noises and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. An evaluation index is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of a pixel with different sizes. Experimental results show that the proposed method can effectively suppress SAR image speckles without compromise the edge sharpness of image features according to both subjective visual examination and objective evaluation indices of image quality.

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (GTD) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided GTD with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method.

Range ambiguity in synthetic aperture radar (SAR) imaging primarily arises from scattered energy of bright targets outside the interested region. So to reduce the ambiguity, we need to identify these targets additionally, which yields an ill-posed problem. To find a feasible solution where the range ambiguity can be sufficiently reduced, we propose in this paper a new method using compressed sensing, a theory which tells when sparse signal can be reconstruct from undetermined linear system, by observing that the recognizable targets are approximately sparse in the ambiguous range zones. Therefore, it is possible to reconstruct the main region and identify the ambiguous targets simultaneously. The simulation results demonstrate the validation of the proposed method.

The first airborne Circular SAR data acquisition experiment of China was carried out in Sichuan by the National Key Laboratory of Science and Technology on Microwave Imaging (MITL), China. Data was acquired using the MITL's P-band, fully polarimetric SAR system along a circular trajectory with the beam spotted on the same area. Compared with the conventional SAR along a straight path, this imaging mode mainly has the following attractive features. First, observing from all directions can help for a better understanding of the scattering properties of targets. Second, the wide angular aperture obtained via flight in a circular track makes possible high resolutions with low frequency band. Third, the aspect angle diversity inherent to the circular track allows for a 3-D target reconstruction. This paper presents the SAR processing of such data, and shows several results to analyze the potentials and limitations of such imaging geometry.

In this paper, an estimation method of micromotion parameters for free rigid targets using micro-Doppler (mD) features is investigated. These parameters include spin rate, precession rate, nutation angle, and inertia ratio. They represent the microdynamic characteristics and intrinsic properties of targets. The time variation of mD frequency is found complicated yet valuable to estimate the micromotion parameters. From the viewpoint of the spectra of mixed mD time-frequency (TF) data sequences, the theoretical analysis and mathematical derivation are conducted in detail according to the scatterer distribution of rigid bodies. We then present an approach to realize the micromotion parameter estimation from radar mD echoes. It mainly consists of TF transform, TF image processing, mixed mD TF data sequence formation, and spectral estimation. Simulation experiments and result discussion are carried out to demonstrate the effectiveness of the proposed estimation method.

The “stop-go” approximation is widely used for the processing of synthetic aperture radar (SAR) data, and the error brought by this assumption can be negligible for most SAR systems. However, for the SAR on a high-speed platform, with the increasing requirements on high-resolution imaging, the error may be intolerable for SAR imaging. In this case, the radar motion within a pulse repetition interval should be taken into account for the echo model and imaging algorithm. In this paper, according to the geometric configuration of the SAR working process, an accurate echo model is presented. By comparing the “stop-go” echo (which denotes the echo based on the “stop-go” approximation in this paper) with the accurate echo, the error brought by the “stop-go” approximation is introduced, and the intolerable error is shown in a reference system. A spotlight imaging algorithm based on the accurate echo is given and is well supported by the simulation results.

The displaced phase center antenna (DPCA) synthetic aperture radar (SAR) has the potential to achieve high azimuth resolution and wide swath. Its pulse repletion frequency (PRF) has to be selected such that SAR platform moves just one half of its total antenna length between subsequent radar pulses. If this condition is not satisfied, there will be nonuniform sampling in azimuth and azimuth ambiguities will appear when traditional imaging algorithms based on matched filter are used. We propose an innovative imaging algorithm based on compressed sensing (CS) which can reconstruct the scene well even though this rigid condition is not satisfied.

There exist bound gravity-capillary waves that move at the speed of dominate waves on the ocean surface, besides free gravity-capillary waves that satisfy the dispersion relation. The radar Doppler spectra are believed to be the sum of two parts: one is attributed to free waves, and the other is attributed to bound waves. The experiments on microwave backscatter from ocean surface in the wind-wave tank are reported in this paper. The occurrence of bound waves can well explain the asymmetry of Doppler spectra between upwind and downwind.

A numerical simulation model of shallow sea topography imaging by Along-Track Interferometric SAR (ATI-SAR) is introduced according to its imaging mechanism. This simulation model is used to study the influence of radar parameters (frequency, incidence angle, baseline length and polarization) on one dimensional sand wave imaging by ATI-SAR. The simulation results indicate that high frequency, VV polarization, an incidence angle of 50° and long baseline length may be favorable to shallow sea topography imaging by ATI-SAR when the imaging time lag between two antennas is shorter than the de-correlation time of the sea.

In this paper, a novel synthetic aperture radar (SAR) imaging method based on L1/2regularization is proposed. Our method implements SAR imaging from compressed measurements with high resolution, enhanced features, reduced sidelobes and suppressed artifacts. Real SAR data experiments are implemented to demonstrate the outperformance of our method. The experiment results demonstrate that our method needs far below the traditional Nyquist rate to guarantee successful imaging. Compared to the prevalent L1regularization-based methods, there is a significant reduction of the sampling rate for SAR imaging. The sampling rate used by our method is about half of the L1regularization-based methods in the real SAR data experiments.

This letter presents a new algorithm for squint circular synthetic aperture radar (SAR) (CSAR) imaging, which is an extension of the well-known range migration algorithm. Due to the circular trajectory, the spatial frequency domain data of squint CSAR cannot be readily obtained via fast Fourier transform, as conventional SAR with straight path does. This method first employs along-track varying system kernels and filters to transform the raw data to the polar spatial frequency domain. Then, it uses an interpolation algorithm to convert the polar samples into rectilinear samples. Implementation aspects, including sampling criteria, resolutions, and computational complexity, are also assessed in this letter. The proposed algorithm is validated both numerically and experimentally.

The aperture of 360° gives circular synthetic aperture radar (SAR) (CSAR) the capability to detect hidden target when its orientation is unknown. Subwavelength resolution can also be achieved when the target in the spotted area is observed under a complete circular aperture. Furthermore, the aspect angle diversity inherent to the circular trajectory makes possible a 3-D target reconstruction. However, the latter two potentials require certain target reflectivity homogeneity. For a highly directive scatterer, it has no resolving ability in the direction normal to the data collection plane. In this letter, a new interferometric CSAR method is presented to enhance the tomographic imaging capability for highly directive scatterers without sacrificing other scatterers' resolutions. This method takes advantage of the coherence and the phase difference between a pair of 3-D SAR images formed from data collected at two separate circular apertures to eliminate targets that focused at a wrong elevation. In addition, it uses two different transmit frequencies to solve the problem of phase cycle ambiguities. Finally, simulation results validate this new approach.

In this paper, we proposed an improvement for the Wishart Freeman classification, which is based on the Wishart distance measurement and the estimation algorithm of the number of clusters for fully polarimetric SAR data. Our experimental results show the effectiveness of the proposed method.

Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments.

In this paper, a set of polarimetric eigenvalue and eigenvector based parameters, e.g. entropy and anisotropy, are investigated for forest application. The correlation terms of the eigenvectors, μ1and μ2, are found to be better for forest mapping in both summer and winter using Radarsat-2 quad-polarimetric space borne SAR data. These are used to automatically identify forest class pixels from the volume scattering category of a Freeman-Durden Wishart unsupervised segmentation map. The algorithm scheme was developed and implemented using fully polarimetric Radarsat-2 SAR (PolSAR) data acquired in July and October and the validity was evaluated using the ground reference data created from SPOT5 K-clustering classification map.

This paper analyses ambiguity suppression caused by multiple-input multiple-output (MIMO) SAR azimuth nonuniform samplings. Two methods are analyzed: azimuth spectrum reconstruction algorithm and minimum mean square error (MMSE) imaging algorithm. The azimuth spectrum reconstruction algorithm can reconstruct the scene fine resolution, while the nonideal orthogonality of multi-channel encoding waveforms causes azimuth ambiguous in SAR imaging. The MMSE imaging algorithm can perfectly reconstruct, while it requires high SNR.

In the context of synthetic aperture radar (SAR) image processing and applications, the precise modeling of statistical knowledge is a crucial problem. In this paper, an efficient and flexible statistical model, called generalized Gamma Rayleigh (G¿R) distribution, for amplitude SAR images is proposed by assuming a two-sided generalized Gamma distribution for the real and imaginary parts of the complex SAR backscattered signal. It is shown that the Rayleigh and recently proposed generalized Gaussian Rayleigh distributions can be regarded as special cases of G¿R distribution. Considering that the probability density function estimation problem is formulated as a parameter estimation one for the parametric statistical analysis of SAR images, a two-stage estimator based on second-kind cumulants is derived for the parameters of G¿R distribution. Furthermore, experimental results on several actual SAR images are given to demonstrate the validity and flexibility of the proposed model.

This paper presented the 3D-SAR with linear array antennas (LAA) which could, in contrast to conventional single-channel 2D-SAR, create the real 3D resolution cells to avoid geometric distortions. Except for conventional side-looking mode, 3D-SAR with LAA can be operated in downward-looking mode which can avoid shadowing effects. The relation between the LAA elements spacing and the elevation angular ambiguity is derived, and the maximal distance between individual antenna elements allowed to avoid elevation angular ambiguity is deduced in this paper. The demonstration of the feasibility of the 3D-SAR with LAA and the relation between elements spacing and elevation angular ambiguity are analyzed by simulation in the last part of this paper.

Interferometric coherence is a key quantity in Polarimetric SAR Interferometry (Pol-InSAR), but it is usually decreased (named decorrelation) by the decorrelation sources, such as polarization type, volume scatterers, instrument settings and processing errors. The analysis of decorrelation has been used to system performance assessment and vegetation parameter estimation. This paper presents two other applications of the decorrelation analysis. One is the derivation of Pol-InSAR system and processing requirements. It is based on quantitative expressions of the decorrelation as a function of system and processing parameters. The other is compensation of the decorrelation that cannot be eliminated by optimizing system and processing parameters. It is implemented by using dual-baseline Pol-InSAR data. The Pol-InSAR requirement on noise equivalent backscattering coefficient and the compensation of temporal decorrelation are shown to demonstrate and validate these two applications respectively.

This paper presented the 3D-SAR based on angle compression principle which could, in contrast to conventional single-channel 2D-SAR, create the real 3D resolution cells to avoid geometric distortions. Except for conventional side-looking mode, 3D-SAR system herein can be operated in downward-looking mode which can avoid shadowing effects. The angle compression principle and the angular ambiguity problem are analyzed in this paper. The analytic expression of angle compression and the condition which should be satisfied to avoid angular ambiguity are also derived in this part. The demonstration of the feasibility of the 3D-SAR based on angle compression principle and the angular ambiguity problem are given by simulation in the last part of this paper.

Three-dimensional imaging radar based on thinned array is investigated in the paper. The multiple phase center aperture synthesis method in time division mode is used to eliminate the high side lobes of thinned array. Using simulated annealing algorithm to optimize the location of antennas, so as to minimize the number of antennas used. Combined with motion compensation, the synthesized phase centers both in quantity and distribution are coincident with a full array. For the cross-track array length is far smaller than the scene width, three-dimensional imaging method is employed with sub-aperture imaging along cross-track. The simulation results denote the validity of the method proposed in the paper.

In this paper, we introduce a new method of terrain slope estimation in the azimuth direction and the ground range direction using only one pass POLSAR data. This method is derived from the polarimetric SAR data compensation for terrain azimuth slopes variation [1] and the Lambertian backscattering model used in the radarclinometry [2]. The AIRSAR L-band POLSAR data are used to show the preliminary results of this method. The comparison of the proposed method and the digital elevation model (DEM) are given to show the effectiveness. The preconditions and limitation of this method are also discussed in this paper.

In this paper, a three-dimensional (3-D) F-SAR imaging algorithm is introduced for Forwarding-looking SAR (F-SAR) data processing. The algorithm is the extension of the two-dimensional (2-D) range stacking algorithm (RSA) and allows the accurate image reconstruction without any interpolation and geometric correction. Then the spatial-varying and spatial-shift properties of the 3-D spread point function (PSF) of Forwarding-looking SAR are revealed with the analytical expression. Finally, the simulation experiment is performed to demonstrate the validity of the 3-D RSA.

In this paper, we consider the super resolution millimeter wave spotlight SAR imaging problem through the simulation under the given Strap-down Inertial Navigation System/Global Positioning System (SINS/GPS) and introduce the Phase Gradient Algorithm (PGA) into the Back-Projection (BP) for SAR focusing. The imaging results indicate that it is feasible to obtain super high resolution imagery at the millimeter wave using the SINS/GPS.

Linear array antennas SAR has a resolving capability in the elevation direction, and can get the 3D image of the target. In this paper, we derive the signal model of 3D SAR using a linear array antenna, and get the 3D resolutions and 3D point spread function of array antenna SAR, at the same time the sampling space of array antennas is given. The variance of the resolution in the elevation direction with array antenna angle and referenced look angle is studied. The geometry to reach the best resolution in the elevation direction is analyzed. Meanwhile the resolution for horizontal and vertical antenna array are calculated and compared.

Spatial spectrum estimation technique is applied to resolve layover effect with multi-baseline synthetic aperture radar (SAR) in the paper. Based on the signal model of multi-baseline SAR, the mathematical principle of layover solution with spectrum estimation is derived. The main steps of spatial spectrum estimation technique for layover solution in multi-baseline SAR are obtained. In order to deal with the spectrum ambiguity problem generated by FFT method under limited multi-baseline SAR data, we introduce the Yule-Walker method for spectrum estimation to resolve layover effect. We analyze the principle of Yule-Walker method for performance improvement, and give the processing steps using Yule-Walker method for layover solution in detail. The simulation results for layover solution with FFT method and Yule-Walker method are realized and compared.

In this paper we improve the valid range analysis method in soil inversion models, using entropy/alpha space in the Cloude-Pottier decomposition theory. The ranges in data where inversion models can be applied are called the valid ranges of the inversion models. The improved valid ranges are considered more accurate through the Integral Equation Method (IEM) simulations. General method used to find out valid ranges of inversion models is the Normalized Difference Vegetation Index (NDVI), which shows the areas where the vegetation over soil is not too heavy for inversion models to apply. The proposed method introduces entropy/alpha parameters to the analysis of valid ranges, because these two parameters are closely related to target scattering mechanisms. Experiment results with fully polarimetric AIRSAR data show that the effectiveness of inversion models is increased by adding entropy/alpha space analysis.

To enhance the computational efficiency and authenticity of Synthetic Aperture Radar (SAR) image simulation of man-made scenes, computer graphics (CG) method was introduced into imaging geometry simulation, graphical electromagnetic computing (GRECO) was used for the interested target scattering calculation. With these two methods, the targets' characteristics will be visualized in simulated image, such as shadow, foreshortening, layover and simple scattering property. The simulated SAR images can be used for target recognition, target detection, system verification and other researches.

Vegetation parameters can be estimated using the single-baseline polarimetric synthetic aperture radar interferometry (POLinSAR) data based on the random volume over ground (RVoG) model. Temporal decorrelation, which is the coherence loss due to scene changes within the time between radar data acquisitions, will decrease the estimation accuracy and needs to be compensated. The RVoG+VTD model is a simple model incorporating a temporal decorrelation term into the RVoG model. The inversion of RVoG+VTD model can not perform due to the limited number of single-baseline POLinSAR observables. Dual-baseline POLinSAR approach provides more observables and hence can be used to invert the model. This paper introduces and analyzes the dual-baseline inversion procedure of RVoG+VTD model and validates them using simulated data.

An estimation of the number of clusters is proposed for fully polarimetric SAR data analysis, and a corresponding unsupervised segmentation algorithm is also given based on the Cloude-Pottier decomposition and the complex Wishart clustering. The Monte-Carlo Cross-Validation (MCCV) is used to estimate the optimal number of clusters to reveal the inner structure of the data. Since it is a quantitative estimation of the classification performance, the MCCV algorithm also has the potential capability to perform the unsupervised segmentation validation. The effectiveness of the MCCV estimation and the segmentation algorithm is demonstrated using ESAR data acquired.

In this paper, the cross validation algorithm is used to estimate the number of clusters for the unsupervised classification of fully polarimetric SAR data. Three different cross validation algorithms are applied for comparison, which are the dispersion measure method, the V-fold cross validation (VFCV) and the Monte-Carlo cross validation (MCCV). Our current experiments show that the dispersion measure method appears generally unable to provide a reliable estimation. The VFCV and the MCCV algorithms seem to be more effective than the dispersion measure method. Moreover, the VFCV is much faster than the MCCV, but the MCCV may be able to provide better estimation than the VFCV.

In this letter, a texture-preserving despeckling algorithm for synthetic aperture radar images using an evidence framework is proposed. The salient aspects of this approach are given as follows. (1) The maximuma posterioriestimate can be guaranteed to converge to the optima by selecting the Gaussian distribution and Gaussian Markov random field model as the likelihood function and prior model, respectively. (2) MacKay's evidence framework can automatically sustain the balance between speckle reduction and texture preservation. (3) We use the Jeffreys prior to perform the second-level inference of the evidence framework. Experimental results are given to demonstrate the validity of the proposed despeckling method.

In this paper, an unsupervised segmentation is proposed for fully polarimetric synthetic aperture radar (SAR) data analysis. The backscattering powerSPANcombined withH/alpha/Ais used to obtain the initial cluster centers. We use the Wishart test statistic to perform an agglomerative hierarchical clustering to obtain the segmentation results with different numbers of clusters. The appropriate number of clusters is automatically estimated using the data log-likelihood (Lm), and the resulting images with the estimated number of clusters are the final segmentation results. The experiments show that theSPANhas additional information that is not contained inH/alpha/A, and this information could be useful for the initialization. The number of clusters seems to be a crucial point for the segmentation, which will affect the segmentation performance. It is also shown that the data log-likelihood has the potential ability to reveal the inner structure of fully polarimetric SAR data.

Based on estimated offset between the corresponding points in the master and the slave image by spectral diversity algorithm, a new indicator misregistration value is proposed to assess registration accuracy. After error analysis is carried out, the new indicator is compared with conventional indicators. Its advantage is the feasibility to assess the registration accuracy in both directions. According to the three indicators, comparison results of several co-registration algorithms are obtained. Airborne X-band interferometric data are used to validate the new indicator.

In this paper, the IHSL transform and the fuzzy C-means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric SAR data. We apply the IHSL colour transform to H/alpha/SPAN space to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/alpha/SPAN. Then the fuzzy C-means algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/alpha/SPAN space directly during the segmentation procedure.

In this paper, a matching method based on feature points matching is proposed. First, a point detector is used to detect interest points of a source image and a target image. Then genetic algorithms (GAs), which are useful in finding global optima, are used to match the detected points. A fitness function which is proven rotation-invariance is proposed to measure the similarity of two points in the paper. According to the results of the GAs, the corresponding points of the target image are found in the source image. The algorithm can overcome the rotation distortion of the images. The experiment results confirm the proposed algorithm is efficient and robust.

In this paper, we introduce a new method to locate the remote images. By using the Geographic Information System(GIS) resources, we can extract the coastlines information. At the same time, by using image processing technologies, we can also extract the similar information from remote images. Based on the image matching technologies, we can roughly locate the remote images. Some simulation experiments are implemented on remote sensing images with coastlines. Preliminary results verify the feasibility of the technique.