Physical parameter estimation of satellite is crucial in space situation awareness as it reflects valuable information. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is a powerful sensor for space surveillance, providing rich information for satellite physical parameter estimation. Parabolic antennas, which are widely loaded in remote sensing and communication satellites, have received great attention recently. Dedicated to the space situation awareness issue using Pol-ISAR, a physical parameter joint estimation method of satellite parabolic antenna with key frame Pol-ISAR images is developed in this work. The core idea is to utilize the mapping relationship between parabolic antenna in 3-D space and its projection ellipse in 2-D ISAR image. Under special observation geometry, the closed-form expressions of parabolic antenna physical parameters are deduced for the first time, providing an efficient way for parameter estimation. Moreover, the abundant information within Pol-ISAR images is mined and utilized. Various polarimetric features are adopted for ellipse extraction and the subsequent physical parameter estimation. Compared with single-polarization channel data, the superiorities of polarimetric feature are validated using electromagnetic simulation data.

Polarimetric synthetic aperture radar (PolSAR) is an active microwave imaging system. Due to the coherence characteristic of PolSAR imaging, inherent coherent speckle noise exists in PolSAR images. The registration of PolSAR images is severely affected by speckle noise. Therefore, we first propose a joint filter that combines Refined-Lee filtering and polarimetric whitening filtering (PWF). The filter first applies Refined-Lee filtering to PolSAR images, which greatly reduces the speckle noise while maintaining high-resolution detailed information of the texture, and then uses PWF to normalize and whiten the polarimetric matrix to further limit the interference of speckle noise. After that, the binary robust invariant scalable keypoints (BRISK) algorithm is used to extract high-quality keypoints from the denoised PolSAR image. Then a novel Siamese Multiscale Attention Network (SMAN) is designed, which uses attention modules to construct feature descriptors with different scales. To fully utilize polarimetric information, we adopt the polarimetric covariance matrix and three polarimetric features as inputs to the network and the Second Order Similarity (SOS) as the loss function to train the network. In the keypoint matching stage, we present to use the symmetric displacement distance to further constrain the keypoint pairs obtained by the initial matching, which improves the accuracy of matching keypoint pairs. Experimental results show that our proposed method can effectively reduce the interference of speckle noise and overcome non-linear differences, geometric distortions, and differences in polarimetric scattering information to achieve accurate PolSAR image registration.

Sidelobe suppression is a crucial preprocessing technique for Synthetic Aperture Radar (SAR) image analysis. The presence of strong scattering targets generates sidelobes that can interfere the targets with relatively weak scattering. The overlapping of multiple strong cross-shaped sidelobes may even generate fake targets, significantly influencing the accuracy of SAR target detection and recognition. Among existing sidelobe suppression methods, the Spectral Reshaping Sidelobe Reduction (SRSR) method has shown promising results. It separates the mainlobe and sidelobes through altering the sidelobe direction while preserving the SAR image resolution. However, this method exhibits limitations in effectively suppressing strong cross-shaped sidelobes. It also introduces additional sidelobes, blurring the surroundings of the scattering points. This paper proposes an improved SRSR method to resolve this disadvantage. It constructs a feature image representing the superposition of sidelobes. This is achieved by analyzing the statistical differences of complex data between orthogonal and non-orthogonal sidelobe regions before and after spectral reshaping. Further modulus selection ensures that the feature image only contains the sidelobe information that needs to be eliminated. The proposed method successfully resolves the drawback of introducing new sidelobes in the original SRSR while achieving better suppression of strong cross-shaped sidelobes. Experimental results on airborne and spaceborne SAR images demonstrate that the proposed method outperforms other state-of-the-art techniques. Improved peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) in both range and azimuth directions and smaller image entropy can be achieved by our method. Due to its superior sidelobe suppression capability, the SAR images processed by our method exhibit significantly improved accuracy in target detection.

In this letter, the low peak sidelobe level (PSL) mismatched filter (MSF) designed for coherent frequency diverse array (FDA) radar using phase-coded waveform is proposed. Considering the space-time coupled characteristic of coherent FDA, the proposed MSF incorporates the equivalent transmit beamforming and low sidelobe range compression simultaneously. The minimization of PSL in MSF design is modeled as a second-order cone programming (SOCP) problem and solved by convex optimization. Furthermore, two modified algorithms are proposed to improve the spatial clutter suppression and Doppler tolerance in original framework. The proposed methods will help to expand the applications of coherent FDA radar in wide-covered and high-resolution observation.

Ship detection in synthetic aperture radar (SAR) images is critical to ocean surveillance and rescue. Although many deep learning SAR ship detection methods have been proposed, the performance of these methods depends on the size and quality of the training samples. To resolve these issues, this letter presents an unsupervised ship detection method in SAR images using superpixel segmentation and cross stage partial network (CSPNet). First, the SAR image is over-segmented into superpixels based on our previously proposed superpixel generation algorithm. Then, the complex signal kurtosis (CSK) and a local superpixel contrast are integrated as a statistical indicator for the automatic identification of ship superpixels and background superpixels, thus leading to generation of training samples. Finally, the segmented superpixels are input to the CSPNet, which can learn a representative feature set with high discrimination ability between ships and backgrounds. Our method can achieve pixel-level detection map rather than the bounding box result. Experiments based on the Gaofen-3 and TerraSAR SAR data demonstrate that our method can achieve above 90% actual detection rate.

Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linear structure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linear structure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, supplies a differentiable characteristic to address the problem for the identification of low, slow and small unmanned aerial vehicles (LSS UAVs). However, the primary challenge for the estimation of m-D parameters is how to separate weak rotation signal from Doppler signal and other interferences. Theoretically, null space pursuit (NSP) is an operator-based signal decomposition approach to decompose a signal into additive subcomponents. The premise of NSP is that two separated components are orthogonal. However, due to the different modulation models, rotation signal, Doppler signal or other interferences could not satisfy the condition. Moreover, traditional multi-order differential operator is not suitable for the decomposition of m-D signal. In thi1s paper, back projection strategy with instantaneous orthogonal NSP (BPIO-NSP) is proposed to distill Doppler signal, and then micro-Doppler NSP (MD-NSP) is jointly developed to separate rotation signal for the identification of LSS UAVs. Firstly, the decomposed component after NSP is applied to the short-time Fourier transform (STFT) to find the segments with instantaneous non-orthogonal property. Secondly, the back projection strategy is developed in BPIO-NSP to acquire the instantaneous orthogonal data, so as to adjust the decomposed Doppler signal with high accuracy. Finally, customized operator is specially constructed in MD-NSP to achieve the required rotation signal from the residue. Simulation results verify the theoretical analysis, and measured data of the fun and UAV detection experiments suggest that the proposed methods could be served for the application of the identification of LSS UAVs.

The interrupted-sampling repeater jamming (ISRJ) countermeasure is researched based on waveform agility and mismatched filter design in this work. By analyzing the effects of transmitted waveform on ISRJ imaging characteristic, the principle of the anti-ISRJ method is proposed. The ISRJ range processing gain and energy can be suppressed by designing the waveform and mismatched filter. Meanwhile, the ISRJ azimuth processing gain can be further restrained by utilizing the waveform agility. The anti-ISRJ problem is formulated and is then divided into several subproblems. The analytical solutions of waveform and mismatched filter are derived based on majorization minimization (MM), and a fast algorithm is proposed by applying a squared iterative method (SQUAREM). The fast implementation of the algorithm is derived, and computational complexity is$\mathcal {O}({N\log N})$. Moreover, to improve the anti-ISRJ performance, the peak-to-average power ratio (PAR) constraint is introduced. Specifically, the waveform design subproblem under PAR constraint is solved, and thus, the PAR waveform can also be designed by the proposed algorithm. The simulation results verify that the proposed joint design algorithm shows significantly faster running speed than the conventional algorithms. The anti-ISRJ performance is superior to the existing methods, and the best anti-ISRJ performance can be obtained by applying the PAR waveform agility and mismatched filter design method.

The holographic subsurface radar (HSR) is recognized as an effective remote sensing modality for the detection of shallowly buried objects with a high-resolution image in plain view. However, subsurface detection with HSR is prone to be impaired by clutter contamination, which often obscures the target response. In this letter, a novel clutter mitigation method combining singular value decomposition (SVD) and response cross correlation analysis is presented. The proposed method first applies SVD to decompose the radar data matrix to a number of singular components. Furthermore, the signal cross correlation characteristics are analyzed to demonstrate that the variance of left singular vectors is directly proportional to the target proportion in radar data. Then, target and clutter subspaces can be identified by maximizing the defined weighted target-to-clutter ratio (WTCR). Results of numerical simulation and laboratory experiments corroborate the effectiveness of the proposed method in reducing clutter while preserving the target image.

Superpixel segmentation is essential to the rapid information extraction from synthetic aperture radar (SAR) imagery. In this letter, we propose a fast multiscale superpixel segmentation method based on the minimum spanning tree (MST), which can generate all scales of superpixels accurately in real time. Therefore, our method has the ability to segment SAR imagery with different scales efficiently and is meaningful for applications that require different levels of SAR image details. Experimental results on two real SAR images demonstrate that our proposed superpixel segmentation method can capture the image information of different levels, resulting in better hierarchical segmentation performance in comparison with other state-of-the-art methods.

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified$\ell _{1}$regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved.

The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, the radar images are considerably interpreted by strong reflections from the rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multi-head attention preliminarily. Then, the generative network will further focus on the target regions and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches.

Frequency agility increases the difficulty of jammers to predict and estimate the carrier frequency and, thus, improves the radar electronic counter-countermeasures (ECCM) performance. In this article, frequency agility is introduced into synthetic aperture radar (SAR). The characteristic of the Doppler history of frequency-agile SAR (FASAR) is analyzed, which shows that the azimuth compression could not be achieved by the classic imaging algorithms. In order to reconstruct the images of interested targets, a three-channel sparse imaging method is proposed based on approximated observation model by using the echo data of different carrier frequencies. By deriving the equivalent observation model, it is concluded that the reconstruction performance can be enhanced by improving the coherence of match filter imaging results in different channels. The image characteristic of different channels in FASAR is analyzed based on the chirp scaling imaging operator. A phase compensation method is then proposed to improve the coherence of images of interested targets in different channels. Specifically, the SAR mode of random frequency agility is proposed to improve the reconstruction performance. Finally, simulations are carried out to demonstrate the effectiveness of imaging methods and ECCM performance. Theoretical analysis and simulation results demonstrate that images generated in random FASAR have better performance than those in stepped FASAR. By performing the phase compensation, the reconstruction performance of small targets can be significantly improved.

The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the synthetic aperture radar (SAR) image quality. The anti-ISRJ methods are studied in this work in order to suppress the ISRJ based on the waveform and filter design for SAR. First, the relationship between the ISRJ and waveform is obtained by analyzing the principle of the ISRJ using the ambiguity function. The ISRJ produces multiple false targets based on the high Doppler tolerance of waveform and the characteristic of the matched filter. Next, a method is proposed to counter the ISRJ by transmitting a phase-coded (PC) waveform with low Doppler tolerance and designing the corresponding mismatched filter. The joint design method is then developed to improve the anti-ISRJ and imaging performance. In the proposed methods, the majorization minimization framework is introduced to solve the nonconvex waveform and filter design problem. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed methods. Simulation results show that the joint design method shows better anti-ISRJ and imaging performance in comparison with the separate design method, but it is more sensitive to the ISRJ sampling duty ratio and period.

A joint waveform and filter design method is developed for suppressing radar pulse compression sidelobe level in this article. The problem is formulated as the minimization of the integrated sidelobe level (ISL) under the constraint of waveform constant modular and filter energy. To control the loss-in-processing gain (LPG), an additional function is introduced to constrain the peak level based on penalty function method. The joint design algorithm is then derived based on the alternating minimization and majorization minimization (MM) schemes. The computation complexity is analyzed and the convergence analysis verifies that the algorithm can converge to a critical point. Specifically, the proposed method is extended to the waveform and filter design for suppressing the peak sidelobe level (PSL). Numerical simulations are carried out to analyze the key parameters, which demonstrate the feasibility of the proposed method. Simulation results show that the ISL and PSL can be significantly reduced with small LPG. Moreover, the proposed method exhibits faster running speed than the existing one and it thus can be applied to longer sequence designs.

For polarimetric synthetic aperture radar (PolSAR) data, the correlation of two polarization channels is sensitive to the target orientation relative to the sensor's illumination direction. In this letter, the concept of polarimetric correlation pattern is proposed to explore this scattering diversity. The core idea is to extend the polarimetric correlation from a fixed angle to the rotation domain along the radar line of sight. A set of new polarimetric features are derived, and three features with high target-to-clutter ratio (TCR) are selected for PolSAR ship detection. The proposed ship detection method mainly contains three steps: the features selection, thresholding procedure, and morphological filtering. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantage of the proposed approach, especially for inshore dense ship discrimination.

In this letter, an orthogonal frequency-division multiplexing (OFDM) chirp waveform diversity design scheme is proposed to achieve better correlation properties. First, the subchirp duration and bandwidth are optimized simultaneously to increase the degree of freedom in waveform diversity. Second, an optimization procedure is proposed based on the idea of Gram-Schmidt orthogonalization. To reduce the problem complexity, the design problem is separated into several subproblems. Each subproblem corresponds to designing an OFDM chirp composite waveform and is optimized by the Pareto optimization framework. Simulation results show that the proposed method outperforms the conventional methods in terms of both autocorrelation peak sidelobe level (APSL) and cross correlation peak level (CPL) under the circumstance of the same mainlobe width.

In this article, we propose a fast superpixel region merging algorithm for synthetic aperture radar (SAR) image segmentation. With our previously proposed adaptive superpixel generation approach (ALFCE), an initial over-segmentation superpixel map for SAR imagery can be obtained. A sketch edge map is used here to eliminate the mixed superpixels to refine the over-segmentation. Then, we focus on rapid superpixel merging for efficient and accurate SAR image segmentation by using the statistical region merging (SRM) framework. This article proposes a new merging order with the consideration of statistical dissimilarity measure and common boundary length penalty, as well as the homogeneity constraint for each superpixel pair. For the merging predicate, we define an adaptive merging threshold according to the image complexity, making the proposed superpixel merging no need to set any merging parameters in advance. Disjoint set is utilized in this article to map the superpixel pairs to pixel pairs for the sake of fast region merging, which has a low computation cost even with the increasing of superpixels. Experimental results on synthetic and real SAR images demonstrate that the segmentation precision of our proposed method can reach more than 85% and also superior to other state-of-the-art methods in terms of computational efficiency.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, introduces significant characteristics to identify small unmanned aerial vehicles (UAVs) in remote surveillance. As opposed to the Doppler signal induced by the translation, m-D signal is comparatively weak and consists of multiple frequency components. In this letter, we propose synchrosqueezing phase analysis (SPA) for the extraction of rotation signal with the multichannel radar. Based on the proposed signal model, this new method not only enables multivariate denoising and sharpening time-frequency (TF) representation, but also concentrates on the energy of rotation signal for the separation. Simulations are employed to demonstrate the validity of the proposed method in extracting the m-D features. Applications on field data further prove the potential in delineating m-D characteristics with higher precision and render that this technique is promising for the identification of small UAVs.

In this letter, a method is proposed to suppress the interrupted sampling repeater jamming (ISRJ) by jointly designing the radar waveform and mismatch filter. The joint design problem under multiple constraints is formulated with the optimization criterion of minimizing the integrated sidelobe levels (ISLs) of waveform mismatch filter output and integrated levels (ILs) of ISRJ signal mismatch filter output. An iterative algorithm is proposed to optimize the waveform and mismatch filter using Lagrange multiplier method and alternating direction multiplier method (ADMM), respectively. Simulation results demonstrate that the proposed method achieves good pulse compression performance while suppressing the ISRJ.

This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use.

Ship detection is an important application in polarimetric synthetic aperture radar (PolSAR) image. A novel saliency detector for PolSAR ship detection has been proposed in this work, which considers ship targets as salient candidates from sea clutter in low and medium sea conditions. Firstly, the similarity test based on polarimetric covariance matrix is applied on each pixel within its neighborhood. Then, saliency feature named Similar Pixel Number (SPN) is generated by calculating the similar samples within its neighborhood. Finally, ship targets can be obtained through proper thresholding procedure and morphological filtering. Experimental studies with two Radarsat-2 datasets validate the advantages of the proposed method.

In this paper, the anisotropic generality of the ionospheric irregularities is incorporated in the generalized ambiguity function (GAF) to evaluate its impact on spaceborne P-band synthetic aperture radar (SAR) imaging. The configuration of the anisotropic ionosphere exhibits as a rod-like structure, which is elongated by anisotropic scale, rotated by magnetic heading and projected by geomagnetic inclination. Aiming at these three parameters, numerical analysis is implemented in terms of the coherence and ambiguous resolution. At last, signal-level simulation is carried out to validate the effectiveness of the numerical results.

Ground-penetrating radar (GPR) has been widely used for antipersonnel mine (APM) detection. However, its efficiency is often impaired by high false alarm rate (FAR) caused by the ground clutters. In this letter, a novel robust principal component analysis (RPCA)-based method is proposed for fast prescreening of APM in GPR image. Taking advantage of low rank and sparse structure of GPR image, the proposed method first adopts an efficient RPCA technique—Go Decomposition (GoDec)—to extract the target image. Then, thresholds are applied to the extracted image to detect the target and reject false alarms. The proposed method enjoys two advantages over traditional methods: 1) the ability of reducing FAR while maintaining high probability of detection (PD) in strong noise and clutter environment and 2) the fast detection guaranteed by the modified GoDec that yields results within several iterations. Extensive simulations and laboratory experiments are conducted to validate the proposed method, and the results are satisfactory (high PDs up to 99% and low FARs).

Due to the speckle noise and complex geometric distortions within SAR images, it is still a challenge to develop a stable method that can produce superpixels with both high boundary adherence and visual compactness with low computational costs at the same time. In this paper, we propose an adaptive superpixel generation approach with linear feature clustering and edge constraint for synthetic aperture radar (SAR) images, which consists of three stages. First, the local gradient ratio pattern of each pixel in SAR imagery is extracted as features, which was previously proposed by us for SAR target recognition and has been proven to be insensitive to speckle noise. Second, we propose to use the feature-ratio-based edge detector with Gauss-shaped window instead of the traditional rectangle-shaped window to obtain the edge strength map and final edges for SAR images. Finally, a modified normalized cut (Ncut)-based superpixel generation strategy is adopted using a distance metric that simultaneously measures both the feature similarity and space proximity. In this strategy, we approximate the similarity measure through a positive semidefinite kernel function rather than directly using the traditional eigen-based algorithm. Therefore, the objective functions of weighted local K-means and Ncuts can achieve the same optimum point by appropriately weighting each point in this feature space, which greatly reduces the computation cost. During the linear feature clustering, the coefficient of variation is used to automatically determine the tradeoff factor between the feature similarity and space proximity, which helps change the superpixel shape and size adaptively according to the image homogeneity. Furthermore, the edge information is also introduced to constrain the clustering for the sake of high boundary adherence. By bridging the local K-means clustering and Ncuts, as well as the benefits of edge constraint, our method not only produces superpixels with good boundary adherence but also captures the global image structure information. Experimental results with simulated and real SAR images demonstrate the effectiveness of our proposed method, which performs better than other state-of-the-art algorithms.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, provides an important signature to discriminate between small unmanned aerial vehicles (UAVs) and other aircrafts or birds in remote surveillance. Compared with the Doppler signal induced by the translation, m-D signal, however, is rather weak and consists of multiple frequency components. In this paper, empirical mode decomposition (EMD) algorithm is applied to addressing the mode-mixing problem in the returned signal. Theoretically, Doppler features are consequently allocated in the first few intrinsic mode functions (IMFs). Rather, the partial components of the subsequent IMFs hold a similar property with the rotation signal. Those components are selected as the input data for the sparse recovery. With the sinusoidal frequency-modulated basis (SFMB), the essence of the recovery problem is converted into 1-D parameter optimization. Then, phase orthogonal matching pursuit (POMP) method is developed for the sparse solution. The proposed method is contrasted with the prevailing approach to solving the mode-mixing problem. Simulation results confirm the theoretical analysis, showing the feasibility in the estimation of m-D frequency. The preliminary findings from the measured data suggest that the proposed method has a potential application in the identification of small UAVs.

Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment.

Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

The classic constant false alarm rate edge detector with a rectangle-shaped filter has been proven to be effective and widely used in polarimetric synthetic aperture radar (PolSAR) images. However, in practical use, the assumption of complex Wishart distribution is often not respected, particularly in heterogeneous urban areas. In addition, as a simple smoothing filter, the rectangle-shaped window is often shown to be easy to incur false edge pixels near true edges. Therefore, its performance is limited. To overcome this restriction, we propose a new edge detector for PolSAR images, which utilizes the spherically invariant random vector product model to estimate the normalized covariance matrix for each pixel, and then replace the rectangle-shaped filter with a Gauss-shaped filter. The performance of our proposed methodology is presented and analyzed on two real PolSAR data sets, and the results show that the new edge detector attains better performance than the classic one, particularly for urban areas.

The active contour model (ACM) is widely used in target detection of optical and medical images, but multiplicative speckle noise largely interferes with its use in synthetic aperture radar (SAR) images. To overcome this difficulty, a region- and edge-based convex ACM with high efficiency is proposed for target detection in small-scale SAR images. Then, a novel detection algorithm, which combines the advantages of a multiscale saliency detection method and the proposed high-efficiency ACM, is presented to address a large-scale and high-resolution SAR image automatically. Target detection experiments in real and simulated SAR images show that the proposed methods outperform classical ACMs and the popular two-parameter constant false alarm rate detector in terms of efficiency and accuracy.

Cross-polarized scattering (HV) is not only caused by vegetation but also by rotated dihedrals. In this letter, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended model-based decomposition method for polarimetric synthetic aperture radar (PolSAR) data over urban areas. Unlike other urban decomposition techniques which need to discriminate between urban and natural areas before decomposition, this proposed method is applied directly on the PolSAR image. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition process. This enables the separation of the cross scattering of urban areas from the overall HV component. The cross and helix scattering components are also compared in this study. RADARSAT-2 quad-pol C band and AIRSAR L band data are used to validate the performance of the proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other urban decomposition techniques.

The presence of multiplicative noise in synthetic aperture radar (SAR) images makes segmentation and classification difficult to handle. Although a fuzzy C-means (FCM) algorithm and its variants (e.g., the FCM_S, the fast generalized FCM, the fuzzy local information C-means, etc.) can achieve satisfactory segmentation results and are robust to Gaussian noise, uniform noise, and salt and pepper noise, they are not adaptable to SAR image speckle. This letter presents a kernel FCM algorithm with pixel intensity and location information for SAR image segmentation. We incorporate a weighted fuzzy factor into the objective function, which considers the spatial and intensity distances of all neighboring pixels simultaneously. In addition, the energy measures of SAR image wavelet decomposition are used to represent the texture information, and a kernel metric is adopted to measure the feature similarity. The weighted fuzzy factor and the kernel distance measure are both robust to speckle. Experimental results on synthetic and real SAR images demonstrate that the proposed algorithm is effective for SAR image segmentation.

Circular cylinders are important classes of environmental and engineering targets which act as depolarizing features, such as pipes, cables, rebar and tree roots. Two-dimensional plan survey using the linearly polarized Ground Penetrating Radar (GPR) is verified as an effective method to obtain the full-resolution image of subsurface objects and architecture. Compared to the regular measurement, a circular measurement using bow-tie antenna is proposed and shown its obvious advantage to map subsurface crossing cylinders with unknown orientations. The configuration of circular measurement is also an important index for obtaining a high-quality image; meanwhile, the proper spatial sampling interval can reduce the data acquisition time which is very important factor in field survey. A field measurement using 500MHz bow-tie antenna was carried out in the sandpit and validated the effectiveness and feasibility of the proposed circular measurement.

Since superpixel takes spatial relationship between pixels into account, which makes the image classification process more understandable and the results more satisfactory, superpixel-based classification methods have been widely studied in recent years. However, due to speckle noise, traditional superpixel generating algorithms still have some drawbacks for synthetic aperture radar (SAR) image. In this letter, we propose a novel superpixel generating algorithm based on pixel intensity and location similarity (PILS) for SAR image. In addition, for the sake of image classification, features of Gabor filters and gray level co-occurrence matrix (GLCM) are extracted from each superpixel. The proposed superpixel generating method has the following three characteristics: (1) the terrain boundaries of SAR image are preserved well; (2) the method has more robustness against speckle noise; and (3) it has high computational efficiency. Experiments on synthetic and real SAR images demonstrate that our method significantly outperforms several state-of-the-art superpixel methods and PILS superpixel-based classification obtains better results than other pixel-based methods.

High radiating efficiency of antenna is very crucial for GPR applications. However, the bow-tie antenna, which is widely used in impulse GPR, has very low radiating efficiency because remarkable energy fed into antenna is radiated as the form of end reflection. In this paper, we study how to design bow-tie antenna with high radiating efficiency for impulse GPR. We find that, if the bow-tie antenna is excited by a bipolar pulse, the radiation efficiency can be significantly improved by utilizing the energy in end reflections. And the improvement is implemented by optimizing the antenna length to superpose the main pulse with the end reflection of a radiated pulse.

In ground-penetrating radar imaging, the classic back-projection (BP) algorithm has an excellent reputation for imaging in layered mediums with convenience and robustness. However, the classic BP algorithm is time consuming and with a lot of artifacts, which have adverse effects on the following work like detection and recognition. A novel BP algorithm, which is both fast and with good effect of suppressing artifacts, is proposed in this letter. At first, an approved approximation method is used to calculate the position of refraction point with remarkable speed and satisfactory accuracy. Then, a lookup table is used to reduce the redundancy in classic BP algorithm. In order to achieve effective artifact suppression, a cross-correlation-based method is introduced. Experimental results of field data present the superiority of the proposed BP algorithm over its classic counterpart both in operation speed and artifact suppression.

With the inverse synthetic aperture radar (ISAR) imaging model, targets should move smoothly during the coherent processing interval (CPI). Since the CPI is quite long, fluctuations of a target's velocity and gesture will deteriorate image quality. This paper presents a multiple-input-multiple-output (MIMO)-ISAR imaging method by combining MIMO techniques and ISAR imaging theory. By using a specialM-transmitterN-receiver linear array, a group ofMorthogonal phase-code modulation signals with identical bandwidth and center frequency is transmitted. With a matched filter set, every target response corresponding to the orthogonal signals can be isolated at each receiving channel, and range compression is completed simultaneously. Based on phase center approximation theory, the minimum entropy criterion is used to rearrange the echo data after the target's velocity has been estimated, and then, the azimuth imaging will finally finish. The analysis of imaging and simulation results show that the minimum CPI of the MIMO-ISAR imaging method is 1/MNof the conventional ISAR imaging method under the same azimuth-resolution condition. It means that most flying targets can satisfy the condition that targets should move smoothly during CPI; therefore, the applicability and the quality of ISAR imaging will be improved.

A real-time recursive back projection (BP) imaging algorithm is presented in this paper. Real time imaging is an intense demand but a challenging task in impulse surface penetrating radar (ImpSPR)'s application. Based on 'delay-sum' operation in time domain, BP imaging algorithm can precisely focus the scattering intensity and obtain high quality subsurface profile. But it's heavy computation burden restricts it's application in ImpSPR's real-time processing. By minutely analyzing it's procedure, a recursive model of BP imaging algorithm is established and real-time BP imaging algorithm is educed subsequently. The computation complexity of both non real time BP imaging algorithm and real time BP imaging algorithm are analyzed. Through processing the experimental data obtained by a ImpSPR system- RadarEye, the imaging algorithm validates its capability at the aspect of ImpSPR's real time processing.