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Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $ Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust. [ABSTRACT FROM AUTHOR]

This paper examines the properties of bistatic scattering from an inhomogeneous rough surface, which, in this paper, is modeled by the transitional layer as a function of depth. The lower medium of the rough surface is horizontally uniform but vertically inhomogeneous. Both linear and circular polarizations are investigated in light of the dependences of transition rate, background dielectric constant, and surface roughness. The presence of dielectric inhomogeneity generally leads to several features that do not appear in the homogeneous surface, such as the scattering coefficient on the whole scattering plane is enhanced; the dynamic range of HH and VV over the azimuth plane is reduced; HV can be greater than VH; and the difference of LR and RR is decreased. With the increasing transition rate, the scattering coefficients for both the linear and circular polarizations are enhanced. As the background dielectric constant increases, the scattering responses of the linear and circular polarizations are quite different. For the linear polarization, HH exhibits a stronger angular dependence; VV reduces in the forward region and enhances notably in the backward region; and HV decreases but VH increases. For circular polarizations, the cross-polarized LR increases in the backward region but decreases in the forward region, and the copolarized RR enhances on the whole scattering plane. With the increasing surface roughness, the scattering coefficient becomes more evenly distributed over the entire scattering plane. [ABSTRACT FROM AUTHOR]

This paper examines the polarized backscattering of spatially anisotropic rough surfaces. To better explore the physical mechanisms that control the azimuthal dependence of the backscattering from anisotropic surfaces, the effects of surface roughness [correlation length and root-mean-square (rms) height], dielectric constant, and radar parameters from anisotropic surfaces are studied. The advanced integral equation model (AIEM) is used to simulate both co- and cross-polarized backscattering coefficients, including the single and multiple scattering. Numerical results suggest that the multiple scattering exhibits a stronger azimuthal dependence for HH than VV polarization, especially more so at a larger incident angle. For weakly anisotropic surface, the azimuthal variation of backscattering tends to be a sinusoidal-like pattern. However, with the enhancement of anisotropy, such a scattering pattern is distorted, and the sharp dip appears at up/down direction. As the rms height and dielectric constant increase, the scattering is enhanced on the whole. The HH/VV ratio at lower dielectric constant is greater than that at higher one. In comparison, scattering shows stronger dependence on anisotropy at lower dielectric constant, especially at a larger incident angle. As an application example, we compare the model predictions with reported measurements from two different sites. Preliminary results are quite encouraging, and thus, the analysis presented in this paper is potentially useful to predict and interpret backscattering from crop field surface, where strong anisotropic surfaces commonly present due to plowing or raking practice. [ABSTRACT FROM AUTHOR]

This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data. ISPs are highly nonlinear due to multiple scattering, and iterative algorithms with regularizations are often used to solve such problems. However, they are associated with heavy computational cost, and consequently, they are often time-consuming. This paper proposes the convolutional neural network (CNN) technique to solve full-wave ISPs. We introduce and compare three training schemes based on U-Net CNN, including direct inversion, backpropagation, and dominant current schemes (DCS). Several representative tests are carried out, including both synthetic and experimental data, to evaluate the performances of the proposed methods. It is demonstrated that the proposed DCS outperforms the other two schemes in terms of accuracy and is able to solve typical ISPs quickly within 1 s. The proposed deep-learning inversion scheme is promising in providing quantitative images in real time. [ABSTRACT FROM AUTHOR]

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman–Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms. [ABSTRACT FROM AUTHOR]

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar and synthetic aperture radar involve the creation of high-resolution images of a scene via scattered signals recorded at different locations. Each pixel of the reconstructed image includes information obtained from multiple aspects due to the changing position of the sources/receivers. In this paper, the aspect-dependent scattering at each pixel is exploited to provide additional information about the scene; this paper presents a framework for converting and utilizing multiaspect data, as well as several examples. For sonar data, as is presented here, the aspect dependence may be leveraged to separate objects of interest from the background, to understand the local bathymetry, or for visualizing acoustic shadowing in full circular synthetic aperture sonar images. Several examples of images of the seafloor containing objects of interest are presented for both circular and linear apertures. In addition, the aspect dependence of low-frequency elastic scattering from objects may be used to understand the underlying scattering physics, which is of potential use in fields such as target recognition and nondestructive testing; a laboratory example is presented. [ABSTRACT FROM AUTHOR]

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [−5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell’s equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer–Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data. [ABSTRACT FROM AUTHOR]

In this paper, we developed and evaluated a phenology-based coherent scattering model to estimate terrain backscatter at the L-band for growing corn. The scattering model accounted for combined effects from periodicity in soil and vegetation, and changes in plant structure and phenology. The model estimates were compared with observations during the two growing seasons in North Central Florida. The unbiased average root-mean-square (rms) differences between the model and observations decreased from 5 to 1.31 dB when these combined effects were included. During the early stage, direct scattering from soil was the primary scattering mechanism, and as the vegetation increased, the interactions between stems and soil became the dominant scattering mechanism. The most sensitive soil parameters were moisture content and rms height, and vegetation parameters were the widths of stems, leaves, and ears, and the stem water content. This paper demonstrates that it is necessary to consider periodicity and plant structural effects in algorithms to retrieve realistic soil moisture in agricultural terrain. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification. [ABSTRACT FROM PUBLISHER]

For radar target detection, the clutter distribution model needs to be identified first. The goodness of fit (GoF) between the original data and the assumed distribution can be used to choose the proper distribution model. Generally, the GoF is obtained using data histogram and theoretical distribution curve, and then the distribution model is judged via GoF. However, when the sample number is small, the histogram is rough and fluctuating, affecting the analysis of GoF. For the small sample, the graphical characteristic is obtained with the sample data to choose the most fitting distribution to the data in this paper. The graphical characteristic is acquired by a simpler process, that is, the original data are directly set as the test statistics, avoiding computing and sorting of other statistics. In this paper, the real airborne circular synthetic aperture radar data under different scan angles are analyzed using the GoF corresponding to histogram and graphical GoF, respectively. The results show that when the sea-clutter data histogram is close to two distributions, a more fitting distribution model may not be obtained by traditional GoF, but can be acquired by graphical representation. In addition, the sea data with different sight angles have different match properties. It is seen that the sea data are closer to the Rayleigh distribution in side-looking mode than that in big squint-angle mode, while the Weibull distribution and K distribution show equal fitting performance to sea clutter under variant radar sight angles. [ABSTRACT FROM PUBLISHER]

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. [ABSTRACT FROM PUBLISHER]

This paper proposes a fast target indexing method based on target geometric models and further conceives a forward method based coarse-to-fine hierarchical synthetic aperture radar (SAR) automatic target recognition (ATR) system. The proposed fast SAR target indexing method is employed for coarse classification before the implementation of fine classification and recognition, resulting in significant acceleration of target recognition. Furthermore, the fast SAR target indexing method is purely based on target geometric models without resorting to any time-consuming electromagnetic (EM) computation, which enables the online and real-time classification. The binary target region and shadow region are selected as the indexing features, and a forward feature prediction method based on optical visibility is developed to predict the features from the target geometric model in real time. Then, the features extracted from the testing SAR image are aligned with the predicted features to establish correlation ship and a fused similarity based on four types of complementary similarity criteria is designed to further implement target indexing. The effectiveness of the proposed fast SAR target indexing method is demonstrated by using the MSTAR dataset. [ABSTRACT FROM AUTHOR]

Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottier’s H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Center’s (DLR)’s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification. [ABSTRACT FROM AUTHOR]

Although the deep convolutional neural network (DCNN) has been successfully applied to automatic target recognition (ATR) of ground vehicles based on synthetic aperture radar (SAR), most of the available techniques are not suitable for inverse synthetic aperture radar (ISAR) because they cannot tackle the inherent unknown deformation (e.g., translation, scaling, and rotation) among the training and test samples. To achieve robust polarimetric-ISAR (Pol-ISAR) ATR, this paper proposes the spatial transformer-multi-channel-deep convolutional neural network, i.e., ST-MC-DCNN. In this structure, we adopt the double-layer spatial transformer network (STN) module to adjust the image deformation of each polarimetric channel and then perform a robust hierarchical feature extraction by MC-DCNN. Finally, we carry out feature fusion in the concatenation layer and output the recognition result by the softmax classifier. The proposed network is end-to-end trainable and could learn the optimal deformation parameters automatically from training samples. For the fully Pol-ISAR image database generated from electromagnetic (EM) echoes of four satellites, the proposed structure achieves higher recognition accuracy than traditional DCNN and MC-DCNN. Additionally, it has shown robustness to image scaling, rotation, and combined deformation. [ABSTRACT FROM AUTHOR]

In order to better interpret polarimetric synthetic aperture radar (PolSAR) images, many scholars tend to do target decomposition for PolSAR images and utilize the obtained features to perform subsequent classification. These target decomposition features play an important role in terrain classification but completely utilizing them produces a high computational complexity. Furthermore, some features have a negative impact on the classification task. Therefore, selecting the appropriate amount of high-quality features is of great significance to the classification task. In this paper, we propose a convolutional neural network (CNN)-based feature selection algorithm for PolSAR image classification. First, we design a 1-D CNN for feature selection, then train the designed network with all the decomposition features to obtain a trained model. Second, the Kullback–Leibler distance (KLD) between different features is utilized as a standard to select feature subsets. Third, feature subsets with excellent performance form the final results. Due to the special structure of the 1-D CNN, repetitively training model is avoided when the input changes. Different from traditional feature selection methods, our method considers the performance of features combination rather than single feature contribution. To this end, the feature subsets selected by the proposed method are more useful to the classification task. Innovatively introducing KLD in the selection stage avoids random selection and improves the selection efficiency. Finally, we validate the performance of selected feature subsets in traditional and deep learning classification frameworks. Experiments demonstrate that features selected by the proposed method have a good performance comparing with others on three real PolSAR data sets. [ABSTRACT FROM AUTHOR]

Feature learning-based polarimetric synthetic aperture radar (PolSAR) classification model will generally suffer from the challenge of deficient labeled pixels. In this paper, we propose a novel generative-discriminative network for PolSAR polar-spatial feature fusion learning and classification, which comprises of a deep generative network and a discriminative network with their bottom layers shared. With this architecture, it enables to make use of both labeled and unlabeled pixels in a PolSAR image for model learning in a semisupervised way. Moreover, the proposed network imposes a Gaussian random field prior and a conditional random field posterior on the learned fusion features and the output label configuration, respectively. Without the need of the complicated recurrent iterations, our network can still efficiently produce the structured fusion feature as well as a smoothed classification map by involving some auxiliary variables, and it is specifically optimized via variational inference within an alternating direction method of multipliers iteration scheme. Extensive experiments on different benchmark PolSAR imageries demonstrate the effectiveness and superiority of the proposed network. Compared with other state-of-the-art algorithms of PolSAR feature learning and classification, our model can achieve a much better performance in terms of the visual quality of the label map and overall classification accuracy, facilitating the much less labeling pixels. [ABSTRACT FROM AUTHOR]

Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix $[T]$ with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , assuming the orientation angle compensation condition, to reduce the number of independent $[T]$ parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for $\text {Re}\{T_{23}\}$ is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix. [ABSTRACT FROM AUTHOR]

Rotational parameter estimation is fundamental to the focusing of imaging algorithm and inverse synthetic aperture radar (ISAR) image size calibration. Previous research has indicated that scattering points (SPs) in different imaging time windows locate at various associated positions in range-Doppler (RD) planes. This paper exploits the relation to estimate the rotation parameters (RPs) and translation error parameters for medium processing time length and nonuniform rotation situation. The iterative closet point (ICP) method inspires an alternating iterative algorithm consisting of the SP assignment and parameter optimization. In the parameter optimization step, multiple SP set pairs are utilized to construct the objective function that represents the degree of mismatch between point set pairs. A compatible fast SP extracting method is also presented to reduce the computational complexity. The experimental results show that the proposed algorithm can provide fast, accurate, and robust estimation for parameters in nonuniform rotation case. [ABSTRACT FROM AUTHOR]

Studies on the potential of using synthetic aperture radar (SAR) imaging data for remote sensing in oil palm growth needs to be done in depth. In this paper, we utilized a scattering model for oil palm to study and understand how L-band microwave interacts with the structure of the oil palm. The model is developed based on the radiative transfer theory and solved iteratively up to the second order. The knowledge obtained can likely be used to interpret the backscattering behavior of oil palm with respect to growth. The oil palm is modeled as a multilayer dense medium with leaflets as elliptical disk while the trunk and fronds as cylinders. The model takes into consideration of the close spacing effects of scattering in a dense medium with the use of phase correction theory. Furthermore, near-field interactions between closely spaced scatterers are also considered via the Fresnel phase and amplitude corrections. Ground truth data were measured from the oil palm fields and are used as input parameters in the model. Five different age groups of oil palm were chosen and their backscatter coefficient simulated at L-band frequency. L-band SAR data acquired were compared with the simulated results, and it was found that there was a good agreement between the simulated results and measured data. For further understanding, the model also simulated the breakdown of backscatter contribution by various components of oil palm which allows a better understanding of the interaction between microwave backscatter with specific components. [ABSTRACT FROM AUTHOR]

Horizontally stratified media are commonly used to represent naturally occurring and man-made structures, such as soils, roads, and pavements, when probed by ground-penetrating radar (GPR). Electromagnetic (EM) wave scattering from such multilayered media is dependent on the roughness of the interfaces. In this paper, we developed a closed-form asymptotic EM model considering random rough layers based on the scalar Kirchhoff-tangent plane approximation (SKA) model that we combined with planar multilayered media Green’s functions. In order to validate our extended SKA model, we conducted simulations using a numerical EM solver based on the finite-difference time-domain (FDTD) method. We modeled a medium with three layers—a base layer of perfect electric conductor (PEC) overlaid by two layers of different materials with rough interfaces. The reflections at the first and at the second interface were both well reproduced by the SKA model for each roughness condition. For the reflection at the PEC surface, the extended SKA model slightly overestimated the reflection, and this overestimation increased with the roughness amplitude. Good agreement was also obtained between the FDTD simulation input values and the inverted root mean square (rms) height estimates of the top interface, while the inverted rms heights of the second interface were slightly overestimated. The accuracy and the performances of our asymptotic forward model demonstrate the promising perspectives for simulating rough multilayered media and, hence, for the full waveform inversion of GPR data to noninvasively characterize soils and materials. [ABSTRACT FROM AUTHOR]

Electromagnetic simulation of fractures has a growing importance in geophysical exploration, where the formation is usually inhomogeneous and modeled as a layered medium (LM). In this paper, we have developed the first integral-equation based solver to simulate the scattering of fractures straddling an LM. We refer to this solver as LM-thin dielectric sheet (TDS)-surface integral equation (SIE), since it is built on the TDS-based SIE and the LM Green’s function (LMGF). Compared with the traditional finite element method (FEM) and volume integral equation (VIE), LM-TDS-SIE achieves excellent efficiency by taking advantage of the multiscale feature of fracture, rather than being restricted by its volumetric mesh. Good accuracy but the lower computational cost of LM-TDS-SIE is well demonstrated by investigating several typical examples, where reference results are obtained by the commercial software COMSOL. In addition to fractures, LM-TDS-SIE can also be used to simulate the scattering from other types of TDS. [ABSTRACT FROM AUTHOR]

Template-matching-based approaches have been developed for many years in the field of synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of template-matching-based approaches is strongly affected by two factors: background clutter and noise and the size of the data set. To solve the problems mentioned above, a multilevel reconstruction-based multitask joint sparse representation method is proposed in this paper. According to the theory of the attributed scattering center (ASC) model, a SAR image exhibits strong point-scatter-like behavior, which can be modeled by scattering centers on the target. As a result, the ASCs can be extracted from SAR images based on the ASC model. Then, ASCs extracted from SAR images are used to reconstruct the SAR target at multilevels based on energy ratio (ER). The multilevel reconstruction is a process of data augmentation, which can not only restrain the background clutter and noise but also augment the data set. Several subdictionaries are designed after multilevel reconstruction according to the label of training samples. Meanwhile, a test image chip is reconstructed into multiple test images. The random projection coefficients associated with multiple reconstructed test images are fed into a multitask joint sparse representation classification framework. The final decision is made in terms of accumulated reconstruction error. Experiments on moving and stationary target acquisition and recognition (MSTAR) data set proved the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The polarimetric two-scale model (PTSM) was introduced a few years ago as an electromagnetic scattering model to be used within algorithms for soil moisture retrieval from polarimetric synthetic aperture radar (SAR) data. PTSM inherits the ability to account for depolarization effects from the original two-scale model (TSM), and, with respect to the latter, it has the advantage to provide closed-form expressions of the elements of the covariance matrix that hold for moderate large-scale surface slopes. This allows a very fast evaluation of scattering, since numerical integration needed by the original TSM is avoided. The TSM, also called composite model (CM), has been extensively used to study scattering from the sea surface, so that it is natural to explore the use of PTSM for the same purpose. However, in its current formulation, PTSM assumes that large-scale surface slope distribution and small-scale roughness spectrum are isotropic. This is not realistic for the sea surface, for which anisotropy is dictated by the wind direction. Accordingly, we here extend PTSM to account for surface roughness anisotropy, so obtaining the anisotropic PTSM (A-PTSM). In addition, as a second contribution, we provide A-PTSM expressions also in the circular polarization basis, which may be useful for some SAR sensor polarimetric configurations. Finally, we compare A-PTSM results with sea surface scattering measurements available in the literature and with results of the second-order small-slope approximation (SSA2). In particular, as a third original contribution of this paper, an analytical closed-form expression of the ratio of crosspolarized normalized radar cross sections (NRCSs) obtained by SSA2 and A-PTSM is provided. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheme for detecting ship targets in high-resolution (HR) single-channel synthetic aperture radar (SAR) images. By using the proposed spatially enhanced pixel descriptor (SEPD) and the modified density-based spatial clustering of application with noise (M-DBSCAN), this scheme can overcome typical challenges of ship detection in HR SAR images. Specifically, the proposed SEPD maps the representation for a given pixel in an SAR image into a high-dimensional feature space by embedding spatial and intensity information of its neighborhood synchronously. It enables the spatial structure information of ship targets and textural information of the sea surface to be preserved by the SEPD feature vector, leading to a significant improvement in the separability between ship targets and sea clutter. A statistical study shows that, in SEPD feature space, a large amount of pixels belonging to sea clutter gather densely in the low-value region and are surrounded by a tiny proportion of ship targets that are distributed sparsely in the high-value region. This distribution characteristic motivates us to apply a density-based clustering approach to distinguish ship targets from the sea clutter. To overcome the weakness of original DBSCAN clustering algorithm and make it suitable for the requirements of ship detection in SAR images, we propose the method of M-DBSCAN, which introduces three critical improvements, including a new dimensionality independent distance metric, a one-class clustering strategy, and an entirely deterministic approach to border points. A novel ship detector is proposed by applying M-DBSCAN to cluster pixels that are represented by SEPD descriptor. Comprehensive experiments demonstrate that the proposed method outperforms other intensity-based clustering methods ($k$ -means and fuzzy c-means), and widely used intensity threshold-based method (constant false alarm rate detector) in most circumstances, and can effectively handle various challenging situations appearing in HR images, such as sidelobes, small/weak targets, moving targets, and so on. [ABSTRACT FROM AUTHOR]

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales. [ABSTRACT FROM AUTHOR]

This paper presents an efficient algorithm to analyze the electromagnetic scattering from a 3-D object buried under a 2-D rough surface. The Poggio–Miller–Chang–Harrington–Wu–Tsai integral equation is implemented in the framework of the characteristic basis function method (CBFM). To expedite the solution, a new approach which utilizes a current-based physical optics (PO) method called the modified equivalent current approximation (MECA) is used for efficient calculation of the characteristic basis functions for the dielectric rough surface. Moreover, the adaptive cross approximation (ACA) is used to accelerate the generation of the reduced matrix during which the interaction between the rough surface and the object is rigorously taken into account. The proposed approach, referred to as CBFM-MECA-ACA, is shown to be particularly efficient when deriving statistical results using the azimuthal angular averaging method. In addition, the range of validity of the proposed algorithm is investigated to demonstrate that both far- and near-field quantities can be accurately derived under appropriate constraints. Finally, the numerical results are presented to show the performance and the versatility of the CBFM-MECA-ACA algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an attempt is made to investigate the angular signatures of bistatic scattering, in the azimuthal direction, from rough surfaces, with the aim of deepening our understanding of the bistatic scattering behaviors and exploring its potential applications. Three distinct angular features, dip angle, scattering strength, and angular width, as a function of the surface roughness and dielectric constant, are identified. Brewster’s scattering, and its role in angular behavior, is examined at limited extent. Results reveal that the angular features strongly correlate with the surface parameters and scattering geometry. For small scattering angle, dip angle and width are independent of surface roughness. Comparatively, for larger incident and scattering angles, beyond 50°, the dip angle and scattering strength are sensitive, simultaneously, to rms height and dielectric constant, while the dip width only responses to rms height. Dips, induced by Brewster’s scattering effect, not only shift in the polar direction, but also in the azimuthal direction, and are strongly dependent on surface parameters and bistatic geometry. Increasing the surface roughness or, equivalently, the incident angle tends to promote the disappearance of dips. The main contributions of this paper can be summarized as follows: 1) quantitative description of dip features, including angle, scattering strength, and angular width; 2) comprehensive characterization of the dip features and their dependence on surface parameters and bistatic geometries; and 3) limited investigation of the behavior of Brewster’s scattering-induced dip. [ABSTRACT FROM PUBLISHER]

Currently, images obtained with microwave synthetic aperture radars (SARs) are widely used for measuring directionality of ocean waves on a global and continuous scale. However, with all the advantages of the microwave SAR systems, the effectiveness of wave spectrum retrieval from SAR images is still debated. In this paper, we demonstrate how the directional spectrum of relatively long sea waves can be measured using aperture synthesis method in conjunction with relatively low radio frequencies (HF and VHF bands). This approach has advantages over HF ground-based radars used for ocean wave studies in coastal zones. In this paper, we theoretically evaluate this technique for the system mounted on aircraft. A favorable combination of the parameters for both ocean surface waves and HF electromagnetic waves allows an accurate analytical description of scattering from the sea surface based on the first approximation of the small perturbation method. In this case, scattered electromagnetic field becomes a linear functional of sea surface wave elevations. An analysis of the signal processing pertaining to this situation is presented, and a practical example is discussed. The proposed approach can be used even in the case when the radar platform is moving nonsteadily and/or along the curvilinear trajectory. [ABSTRACT FROM PUBLISHER]

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, −0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. [ABSTRACT FROM PUBLISHER]

A theoretical understanding of a multifrequency microwave approach to understand complex microwave interactions from a highly saline snow cover on a relatively smooth first-year sea ice is presented. We examine the sensitivity of Ku-, X-, and C-band \sigma {\text {VV}}^{0} and \sigma {\text {HH}}^{0} to variability in snow geophysical properties such as salinity, density, temperature, and snow grain radius, sampled from a highly saline snow cover on first-year sea ice. A first-order multilayer snow and ice backscatter model is used to calculate \sigma {\text {VV}}^{0} and \sigma {\text {HH}}^{0} by taking into account the surface and volume scattering contributions within each snow layer of the snow pack. Penetration depth models are used to calculate the potential penetration of all three frequencies, at initial and perturbed snow property conditions. Sensitivity analyses suggest that variability in salinity and snow grain radius have the greatest effect, followed by density and temperature. This phenomenon is observed for all three frequencies, influencing microwave penetration and backscatter. Dielectric loss associated with highly saline snow covers and substantial changes in scattering contributions from snow grain radius perturbations were found to be the dominant factors affecting microwave penetration and backscatter. Results from this paper demonstrate the potential of using a multifrequency theoretical approach to correlate with active microwave observations to determine the geophysical and electrical state of snow/sea ice system. The paper also represents an evolution in a theoretical understanding on how an active microwave approach using multiple frequencies can be further utilized toward the development of snow thickness and/or snow water equivalent algorithm on smooth FYI. [ABSTRACT FROM PUBLISHER]

This paper presents an electromagnetic modeling of temporal variations of microwave backscatter from rice canopy based on radiative transfer theory to understand the complex microwave scattering mechanisms of rice crops at different growth stages. Model validation is made by a comparison with field measurements from four independent campaigns by ground-based scatterometers and spaceborne synthetic aperture radar (SAR). Then, the frequency responses and angular dependence are examined, followed by an analysis of parameter sensitivity and uncertainty to microwave backscatter. The validation shows promising results for the physically based model in assessing radar response of rice plant in its full-growth stage. It is found that radar scattering of rice canopy is highly dependent on the growing stages of rice plants, in addition to radar configurations, such as frequency, polarization, and incident angle. Moreover, backscattering of rice canopy is more dependent on stems than on leaves, and as rice plants grow, increasing stems and leaves tend to promote HH- and VH-polarized scattering, while first promoting and then reducing VV-polarized scattering. Moreover, the greatest uncertainty takes place at the late growth stage for copolarization and at the early stages for cross-polarization. The main contributions of this paper can be summarized as follows: 1) the establishment of a rice canopy microwave backscattering model using radiative transfer theory adapted to all the stages in the phenological cycle of rice; 2) the validation of the performance of the proposed model with numerous independent measurements from spaceborne SAR and ground-based scatterometers from different countries and regions; 3) the investigation of the interactions between microwave backscatter signatures and rice canopy growth variables over growth stages; and 4) the identification of the dominant influential parameters of the rice scattering model and determination model uncertainty. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel data filtering algorithm for fully polarimetric synthetic aperture radar (PolSAR) based on the degree of polarization (DoP) information. First, we define the homogeneity degree and polarization independence degree using the DoP information, and propose a feature plane to characterize the target feature. Second, employing the feature plane, we categorize the targets into three types and assign specific filtering policy for each type to estimate the optimal filtering window sizes. Finally, the $T$ -matrices of fully PolSAR data are filtered using the windows with estimated optimal sizes. Compared with boxcar filter, refined Lee filter, scattering model-based filter, and improved sigma filter in processing ALOS2-PALSAR2 data, the proposed DoP-based algorithm presents the best filtering performance. [ABSTRACT FROM AUTHOR]

Freeman–Durden decomposition is a frequently used technique to analyze the scattering characteristics of multilook Polarimetric Synthetic Aperture Radar (POLSAR) data. When it is applied to the real POLSAR data, two problems emerge, namely, the volume scattering overestimation and negative powers. Many researchers think these two problems are caused by the insufficient decomposition algorithm, and several improvements are proposed. However, the improved decomposition algorithms become more and more complicated, and some new problems such as the decomposed component is not model based also emerge. In this paper, we try to solve the two problems through another way. We think they are caused not by the insufficient decomposition algorithm but by the dogmatic input. Freeman–Durden decomposition explicitly assumes reflection symmetry. Its input is a direct truncation of the measured coherency matrix. The truncation can be regarded as a reflection symmetry approximation (RSA) of the measured coherency matrix. We first show some reasons why we do not think the truncation is a good RSA. Then, a new RSA is proposed based on the sum of three reflection symmetry components derived from the measured coherency matrix. Experimental results with several real POLSAR images show that, if the new RSA is used as the input of Freeman–Durden decomposition, the above-mentioned two problems no longer exist. [ABSTRACT FROM AUTHOR]

The determination of the accurate composition ratio of scattering mechanisms (volume scattering, double-bounce scattering, and surface scattering) within a radar backscatter is essential to validate current polarimetric decomposition techniques. Multiincidence angle and multipolarimetric synthetic aperture radar (MIMP SAR) observations at the X- and L-bands were applied to rice paddies at late vegetative stage in Niigata City in Japan in 2014 and 2016, respectively. In this paper, multifrequency MIMP SAR analysis is introduced based on the observation results. The approach, combined with theoretical characterization of the data by a discrete scatterer model, showed that rice panicles affect the backscatter from rice paddies. Contrary to expectation, an effect of transmissivity by using different bands is not obvious. The similar level of copolarization (HH and VV) backscatter at X- and L-bands could be explained by the effective size of rice panicles. They are the most characteristic scatters in rice paddy field with respect to multiple frequency polarimetric sensing. In addition, HV shows a distinct sensitivity to the mean orientation angle and the size of panicles regardless of the wavelength. The mean orientation angle affects the polarimetric randomness under azimuthal symmetry, whereas the size of panicles directly affects the attenuation of the volume scattering from the grains. The multifrequency MIMP SAR analysis also indicated the importance of considering the backscatter and attenuation in the interpretation of the backscattering cross section from vegetated fields. [ABSTRACT FROM AUTHOR]

To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing. [ABSTRACT FROM AUTHOR]

High-precision parameter estimation is crucial for micromotion feature acquisitions. A method of high-precision range estimation based on phase of wideband radar echo is proposed in this paper. After the proposed processing, unwrapped signal phases without any ambiguity can be obtained to calculate the relative range of the target. No constraint condition for phase ambiguity resolution is imposed on the proposed method, which means that it could still work well in low signal-to-noise ratio situations. Cramér–Rao lower bound for the root-mean-squared error of the range estimation is derived in analytical expression. Results of several simulations and experiment with FEKO generated data are presented to show the effectiveness and antinoise performance of the proposed method. [ABSTRACT FROM AUTHOR]

Imaging of a ground moving target (GMT) with synthetic aperture radar (SAR) is a challenging task, particularly when the Doppler ambiguity happens. This paper proposes a novel GMT range model and a novel Doppler ambiguity-tolerated GMT imaging algorithm based on modified keystone transform. In the proposed GMT range model, the range from radar antenna to the scattering center on the target is divided into two parts: the range from radar antenna to the target centroid and the projection length on the light of sight from the target centroid to the scattering center. Based on the new range model, a Doppler ambiguity-tolerated range cell migration correction (RCMC) method is proposed, in which keystone transform is modified to realize the differential RCMC by interpolation with nonzero phase sinc kernel. Finally, the GMT image is obtained in the range-Doppler domain by matched filtering in the slow-time domain and is restored into the 2-D space domain. The effectiveness of our proposed model and imaging method is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme. [ABSTRACT FROM AUTHOR]

The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix are used as the main research object to extract features. In this paper, we focus on the original polarimetric scattering matrix and propose a polarimetric scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on the convolution network to combine this feature. Based on the polarimetric scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for polarimetric scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding. [ABSTRACT FROM AUTHOR]

In this paper, a method for digital elevation model (DEM) extraction over forest areas from single-baseline interferometric synthetic aperture radar (InSAR) data is proposed. The main idea of this method is that some backscattering variations which are linked to the geometrical structures of forest occur during the radar acquisition. The time–frequency analysis is used to retrieve these variations by dividing the synthesized SAR image into multiple SAR images in the Fourier domain called sublook images. Then, by interferometry, the sublook images characterized by the same Doppler bandwidth and acquired from spatially separated locations at either end of a baseline are used to estimate the sublook coherences and the above backscattering variations are converted into the variations of sublook coherences. As a result, the number of InSAR observations can be increased. The sublook coherences are then interpreted by the two-layer vegetation scattering model and are assumed to follow a near-linear relationship in the complex plane. The ground phase can then be estimated by linear regression of the sublook coherences. The performance of the proposed method was validated by E-SAR L- and P-band SAR data acquired over coniferous and tropical forests. For the coniferous scenario, the underlying DEM estimated by the proposed method has a root-mean-square error (RMSE) of 4.39 m, which is slightly less accurate than the DEM (with an RMSE of 4.07 m) derived by the polarimetric line-fit (LF) method, but represents a significant improvement in DEM accuracy over the HH InSAR method. For the tropical scenario, the DEMs derived by the proposed method and the polarimetric LF method are closer to the ground surface than those derived by the HH InSAR method, and their mean ground height difference is 0.62 m. The two experiments confirm that it is feasible to extract a DEM by the proposed method, which has a comparable performance in DEM inversion to the polarimetric LF method and only requires single-polarization InSAR data. [ABSTRACT FROM AUTHOR]

Most applications of radar imagery require processing techniques which achieve one fundamental goal: characterize and detect the constituent scatterers for each pixel in the scene. In this paper, we take a new look at the target detection issue in polarimetric synthetic aperture radar data and assume several canonical scattering mechanisms as our signal sources whose combination of them with appropriate weight fractions formed the scattering vector of each pixel. The presence of speckle as a consequence of coherent processing of the scattered signals is modeled as signal-dependent additive noise. The set of the scattering mechanisms is divided into two groups: objected scattering mechanism belonging to the target, and nonobjected scattering mechanisms. Then, we make use of two techniques based on subspace projections for speckle reduction and the nonobjected scattering mechanisms annihilation, followed by detecting the presence of the scattering mechanism of interest. In the problem formulation scenario, a novel feature space is proposed consisting of two subspaces—the objected subspace and the nonobjected subspace. Then, the detection approach under this scenario is derived. An orthogonal subspace projection technique is utilized for speckle reduction. Moreover, in order to annihilate the nonobjected subspace, each pixel’s feature vector is obliquely projected onto the objected subspace. With the annihilation of the nonobjected subspace and using the polarimetric information of the objected subspace, the detectability of the target scattering mechanism is therefore enhanced. Finally, evaluation against C-, L-, and P-band fully polarimetric SAR data sets is provided with a significant agreement with the expected results. [ABSTRACT FROM AUTHOR]

The electromagnetic scattering generated by agricultural tilled soils can be affected by a strong anisotropic component of the rough-surface profile. An accurate and reliable modeling of the normalized radar cross section, under both monostatic and bistatic geometries, is particularly important and desirable, especially for the correct estimation of the soil moisture content by means of satellite-based observations. In this paper, moving from the modeling so far proposed in the literature, we present and discuss a novel, more general (i.e., 2-D), spectral representation of an agricultural tilled soil, implementing a solution of the scattering based on the first-order small-slope approximation. Comparisons are given with a well-established model based on a 1-D representation of the soil correlation function, accounting for the radiation pattern of the illuminating antenna. The investigation gives new insight on the phenomenology of the bistatic scattering from the anisotropic soil, providing interesting information for the next generation of satellite missions, which foresees the possibility of launching companion satellites carrying aboard a passive receiver collecting the signal transmitted by active SAR-based platforms. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a viable tool for fast and high fidelity detection of concealed explosive threats. The radar effectiveness is limited by scattering from rough terrain which considerably obscures the buried target response. To calculate the rough ground scattering, a 3-D full-wave algorithm such as finite-difference frequency-domain (FDFD) method is required but is often prohibitive for multiple frames when the GPR antennas are distant from the target region. This paper presents a real-time 3-D modeling of a moving platform forward-looking GPR scattering from rough terrain located at great electrical distances from the GPR antenna. For a synthetic aperture, the computational domain of the focal region is reduced to a very small subset of the entire observed volume, and the surface clutter is computed via a mere multiplication of a precomputed impulse response matrix of the rough ground with the matrix characterizing the GPR transmitting signal. For a vehicle-mounted GPR detection system, this results in a significant reduction of complexity and saving of computation resources. The effectiveness of the algorithm is evaluated through an implementation of 3-D Monte Carlo simulation for various rough surface parameters. Our developed model compares well with the direct FDFD results, and can be used for lossy and frequency-dispersive soils. [ABSTRACT FROM AUTHOR]

Global navigation satellite system reflectometry (GNSS-R) receivers produce delay-Doppler maps (DDMs) by incoherently integrating coherent integration results. Due to system dynamics, during incoherent integration, the receiver aligns each coherent result by tracking the delay and Doppler of the specular point. This is known to cause a blurring of the spatial footprint of the Woodward ambiguity function (WAF) on the reflecting surface. In this paper, we demonstrate that the blurring of the WAF varies over the glistening zone (GZ), and even if a fixed point on the ground is tracked, blurring still occurs. We derive the expressions for the delay and Doppler change rates over the GZ and then predict the error introduced by range walk for typical GNSS-R scatterometry configurations. We find that ≈6 dB of loss is expected for a point scatterer near the edge of the GZ when a fixed point on the surface is tracked. The incoherent range walk compensation (IRWC) method is then presented for GNSS-R receivers to mitigate this loss. The IRWC method focuses the power in the DDM to the isodelay and iso-Doppler configuration occurring at the midpoint of the integration time. DDMs produced by tracking a fixed point with and without IRWC are simulated, and errors are found to be in agreement with those predicted. Spatial domain GNSS-R products will be improved with IRWC. Target detection will benefit from a larger usable swath, allowing longer tracking and detection times as a result of the increased target to clutter and noise ratio. At the same time, imaging applications will no longer suffer from a spatially variant blurring of the WAF, which limits the resolution of the estimated products. IRWC is shown to mitigate the range migration losses and improve the SNR of an imaging GNSS-R receiver by ≈6 dB near the edge of the GZ. [ABSTRACT FROM AUTHOR]

A joint petrophysical and structural inversion method for electromagnetic (EM) and seismic data based on the volume integral equation (VIE) is proposed in this paper. In the forward EM problem, only the contrast of conductivity is solved by the electric field integral equation method. However, in the forward seismic problem, both the contrasts of velocity and mass density are solved by the combined field VIE method. Both forward solvers are accelerated by the fast Fourier transform. In the inversion problem, by using the petrophysical equations about the porosity and saturation and applying the chain rule, we fuse the EM and seismic data and construct the joint petrophysical inversion equations, which can be solved by the variational Born iteration method. Then, in order to further enhance the reconstructed results of the joint petrophysical inversion, we enforce the structural similarity constraint between porosity and water saturation and add the cross-gradient function to the joint petrophysical inversion cost function. Two typical geophysical models based on the remote sensing measurement are used to validate the proposed methods. One is the cross-well model, and the other is the marine surface exploration model. The advantage of the joint inversion compared with the separate inversion is evaluated based on the resolution and the data misfits of the reconstructed profiles as well as the antinoise ability. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in remote sensing image processing. In recent years, stacked auto-encoder (SAE) has obtained a series of excellent results in PolSAR image classification. The recently proposed projective dictionary pair learning (DPL) model takes both accuracy and time consumption into consideration, and another recently proposed semicoupled dictionary learning (SCDL) model gives a new way to fit different features. Based on the SAE, DPL, and SCDL models, we propose a novel semicoupled projective DPL method with SAE (SAE-SDPL) for PolSAR image classification. Our method can get the classification result efficiently and correctly and meanwhile giving a new method to fit different features. In this paper, three PolSAR images are used to test the performance of SAE-SDPL. Compared with some state-of-the-art methods, our method obtains excellent results in PolSAR image classification. [ABSTRACT FROM AUTHOR]

In this paper, a time series of 33 TerraSAR-X copolarized synthetic aperture radar (SAR) imagery collected in Stripmap mode over the Gulf of Mexico in a wide range of incidence angles and sea-state condition is exploited, together with a theoretical framework based on the X-Bragg scattering model, to analyze the effects of noise, angle of incidence, (AOI) and wind speed on the standard deviation of the copolarized phase difference ($\sigma _{\phi _{c}}$) evaluated over sea surface with and without oil slicks. This large data set represents an unprecedented opportunity to analyze, for the first time, the influence of both SAR acquisition and surface parameters on the broadening of the copolarized phase difference probability density function (pdf), $p_{\phi _{c}}(\phi _{c})$. Experimental results show that the X-Bragg scattering model, here adopted to predict the sea surface $p_{\phi _{c}}(\phi _{c})$ , gives an understanding of the increasing trend of $\sigma _{\phi _{c}}$ with respect to AOI. It is shown that the noise significantly contributes to broaden $p_{\phi _{c}}(\phi _{c})$ over both slick-free and slick-covered sea surface, while the effects of low-to-moderate wind regimes are negligible. In addition, $\sigma _{\phi _{c}}$ exhibits a larger sensitivity to the scene variability, if compared to single-polarization intensity channels, over both slick-free and oil-covered sea surface. This sensitivity is more pronounced at lower AOIs due to the higher noise equivalent sigma zero (NESZ) that affects larger AOIs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical polarization feature generation using a self-organizing codebook to realize unsupervised land classification that fully utilizes the detailed polarization information contained in high-resolution polarimetric synthetic aperture radar (PolSAR) data. PolSAR has reached a decimeter-level high resolution. In general, conventional methods lower the resolution of the PolSAR data to 10–20 m in the real-space distance to classify observation regions into land classes such as farm, forest, and town. However, lowering resolution prevents us from discovering new land classes potentially enabled by the resolution enhancement. The hierarchical method we propose here not only classifies observation regions successfully into land classes such as farm, forest, and town that humans can naturally distinguish but also discovers new land subclasses findable only in high-resolution PolSAR data. We explain these two types of our achievements (classification/discovery) through experimental results for Japan Aerospace Exploration Agency’s polarimetric and interferometric airborne SAR-L2 data having decimeter resolution. [ABSTRACT FROM AUTHOR]