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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]

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]

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]

With three coherent synthetic aperture radar images, it is possible to form three interferograms. In some cases, the phases of the three averaged interferograms will be significantly inconsistent and indicate a sort of phase excess or deficit (which we call lack of triangularity or inconsistency). In this paper, we illustrate theoretically which models can explain such phenomenon and provide some real-data examples. It is also shown that two or more independent scattering mechanisms are necessary to explain phase inconsistencies. The observation of lack of consistency might be useful to derive information on the target and as a warning that the scatterer presents a temporal covariance matrix, which is not intrinsically real, with consequences for the processing of interferometric stacks. [ABSTRACT FROM PUBLISHER]

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]

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 investigates the bistatic scattering from randomly corrugated surfaces with irregular grooves. For a given incident wave, the surface fields and the scattered field were computed by the method of moments in which the rooftop basis function was used to account for fast phase changes due to steep surface slopes. The total scattered field is decomposed into coherent and incoherent components to analyze their respective contributions. We found that, for randomly corrugated surfaces with irregular ridges or grooves, the coherent scattering is profound at several scattering angles with strong main lobes, whose beamwidths are strongly correlated with the ridge density. The numerical simulation has shown that the lobe angular shift away from the specular direction is quasi-linearly dependent on the ridge density, suggesting that the coherent scattering pattern substantially contains the surface geometric information. We expect this paper to offer deeper understanding of coherent imaging of rough surface and to help in designing a novel imaging system. [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]

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]

Scattering from rice leaves contributes substantially to total vegetation canopy backscattering and detailed knowledge about it is necessary for developing a microwave scattering model. A parabolic curve is generally adopted to simulate the leaf shape but this is rarely incorporated into the calculation of the scattering. In this paper, two specific models, one based on physical optics (PO) approximation and the other on the discrete dipole approximation (DDA), are presented to involve the parabolic leaf curvature effects. Three typical leaves were chosen from 1433 parabolic leaves obtained during ground measurements. The PO and DDA models were used to calculate the leaf scattering. The generalized Rayleigh–Gans (GRG) approximation was also included in the simulation. The method of moments, a computational electromagnetic method, was utilized to evaluate the accuracy of each model. Validation of the models was conducted at incidence angles ranging from 10° to 60°, incidence azimuthal angles ranging from 0° to 360°, and incidence frequencies of 1.2 GHz (L-band), 5.4 GHz (C-band), and 9.65 GHz (X-band). Among the GRG approximation, the DDA model and the PO model, the latter gave the best accuracy −>65% in the cases tested, while the GRG model was the least accurate. The high accuracy of the PO model was maintained at both the low and high frequency bands. The PO model, therefore, has great potential for use to interpret radar measurements from rice fields and other types of vegetation canopy. [ABSTRACT FROM PUBLISHER]

This paper presents the numerical method, generalized propagation-inside-layer expansion (GPILE), to calculate the scattered electromagnetic field by any stratified 1-D medium composed of three random rough interfaces separating homogeneous media. GPILE is a generalization of the propagation-inside-layer expansion method, which considers only two interfaces. Both methods rely on the rigorous implementation of the Maxwell equations, with a simple matrix formulation and which have a straightforward physical interpretation. In particular, this method allows us to distinguish the primary echo of the upper surface and also the multiple echoes arising from the intermediate and lower interfaces. This method is applied in this paper to simulate the ground-penetrating radar (GPR) signal at nadir. The simulated signals are analyzed to study the sensitivity of the GPR signal to any material debonding within the pavement layered structure. [ABSTRACT FROM PUBLISHER]

This paper completes the fundamental development of the basic coherent entities in radar polarimetry for coherent reciprocal scattering involving polarized wave states, antenna states, and scattering matrices. The concept of antenna polarization states as contravariant spinors is validated from fundamental principles in terms of Schelkunoff’s reaction theorem and the Lorentz reciprocity theorem. In the general bistatic case, polarization states of different wavevectors must be related by the linear scattering matrix. It is shown that the relationship can be expressed geometrically, and that each scattering matrix has a unique complex scalar invariant characterizing a homographic mapping relating pairs of transmit/receive states for which the scattering amplitude vanishes. We show how the scalar invariant is related to the properties of the bistatic Huynen fork in both its conventional form and according to a new definition. Results are presented illustrating the invariant $k$ for a range of spheroidal Rayleigh scatterers. [ABSTRACT FROM PUBLISHER]

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]

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]

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with the assignment of persistent scatterers (PSs) to buildings represented by a 3-D city model. Affiliating PSs to real-world structures is highly relevant for two reasons. First, it facilitates interpretation of PS deformation estimates and can be even used to improve those estimates using advanced deformation models for complete structures. Second, such an assignment enables the compilation of a PS density map for the structures under investigation. This helps to mitigate the problem of opportunistic sampling, which is one of the major drawbacks of PS interferometry (PSI). Specifically, a framework in which the PSs are geometrically aligned with the city model and assigned to the bounding surfaces of the single models is outlined. Cases where such an assignment cannot be established are attributed to generalization of the city model or to locations where the reflection mechanism inducing the PS does not correspond to a real-world structure. From the assignments, a density map is compiled, and the main factors driving the PS density are identified and discussed: surface structure, shadowing, aspect dependency, and quasi-random effects (e.g., temporary changes due to building renovation activities). The test site for this paper is located in the inner city area of Berlin. PS results of two high-resolution spotlight TerraSAR-X data stacks of different pass directions are used. The buildings are represented by a level-of-detail 2 city model and an airborne light detection and ranging data set. [ABSTRACT FROM AUTHOR]

Recently, we have proposed a retrieval technique based on an original polarimetric two-scale model (PTSM), which is able to estimate the volumetric water content of bare soils from polarimetric synthetic aperture radar (SAR) data. In this paper, to extend the field of application of our retrieval technique to moderately vegetated soils, we combine the PTSM with a randomly oriented dipole-cloud volumetric scattering model, thus obtaining a polarimetric two-scale two-component model (PTSTCM). By using this model we show that, in principle, suitable combinations of the polarimetric SAR channels, i.e., “modified copolarized ratio” and “modified copolarized correlation coefficient,” are related only to the surface parameters because the dependence on the unknown volumetric contribution intensity cancels out. This allows us to retrieve soil moisture from L-band SAR data not only for bare soils but also in moderately vegetated areas, interested by a nonnegligible volumetric scattering contribution, provided that the double-bounce scattering component is negligible. In addition, describing the surface component by using the PTSM allows us to mitigate the well-known problem of overestimating the volume component, which affects most model-based target decompositions and that may lead to the so-called “negative power problem.” Both the performance and validity limits of the estimation method are assessed by comparing the obtained soil-moisture retrieval results to “in situ” measurements. To this aim, data from SMEX'03 and AGRISAR'06 campaigns available in literature are considered. They refer to sites with a flat topography. In particular, we employ the AGRISAR database, which includes data from several fields covering a period that spans all the phases of vegetation growth, to explore the validity range of the method in terms of vegetation height. Results of PTSTCM are also compared with those of available three-component methods (3CMs) employing more simplified surface scattering models. It has turned out that the use of the PTSTCM provides more accurate results for low vegetation (average modulus of soil moisture relative error for vegetation height smaller than 50 cm: 18.5% for the PTSTCM and 34% for the 3CM). Conversely, for higher vegetation, 3CMs should be more conveniently employed (average modulus of relative error for vegetation height greater than 50 cm: 17.5% for the 3CM and about 100% for the PTSTCM). A simple method to adaptively and automatically (i.e., based on measured data) select between PTSTCM and 3CM on a pixel-by-pixel basis is finally suggested, leading to a less than 20% average modulus of relative error on the retrieved soil moisture for the considered fields over the entire vegetation growth cycle. [ABSTRACT FROM PUBLISHER]

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]

The reconstruction of a source embedded within a multipath environment, which is created by inserting a grid of point scatterers in the scene, is addressed. In particular, the source Fourier spectrum is assumed known so that the focus here is on the reconstruction of the spatial support. As well documented, multipath can allow for resolution improvement. However, it also gives rise to artifacts when a backpropagation-like imaging is adopted. In this paper, we study in detail how resolution improvement and artifacts depend on the grid layout by employing a weighted backpropagation algorithm. More in detail, stationary phase arguments are used to predict the reconstruction leading order terms to which resolution improvement is linked. Moreover, it is shown that artifacts are mainly due to high-order terms and are dependent on the point scatterers’ arrangement. The nature of such artifacts is studied, and a simple way to mitigate their role (without resolution loss) is introduced; it consists in a suitable nonuniform grid arrangement with a “hole in the center.” Backpropagation is then compared with an inverse filtering imaging based on the truncated singular value decomposition (TSVD) of the radiation operator. It is shown that the TSVD is less prone to artifacts and can, in principle, allow for a higher resolution improvement. However, when model error (due to multiple scattering between the elements of the grid) and/or noise corrupt data, backpropagation performs definitely better. The theoretical findings are supported by an extensive numerical analysis. In particular, to keep the figures simple, we consider only 2-D cases. [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]

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band. [ABSTRACT FROM AUTHOR]

Non-Rayleigh distributed radar clutter is widely reported in studies of radar scattering from sea and land surfaces. Existing models of scattered field amplitude distributions have been developed primarily through empirical fits to the statistics of radar backscatter measurements. In contrast, this paper investigates a physics-based approach to determine the amplitude distributions of fields scattered from rough surfaces using Monte Carlo simulations and analytical methods, for both backscattering and bistatic configurations. The rough surface is represented using a “two-scale” model. An individual surface facet contains “small-scale” roughness, for which scattered fields are evaluated using the second-order small slope approximation. Individual surface facets are tilted by the slopes of the “large-scale” roughness in a given observation. The results show that non-Rayleigh amplitude distributions are obtained when tilting is performed, and that the departure from the Rayleigh distribution becomes more significant as the variance of the tilting slope increases. Further analysis shows that this departure results from variations in the mean scattering amplitude from a facet (the texture) as tilting occurs. The distribution of the texture is studied and compared with existing models. Finally, the distribution of the scattered field amplitude is modeled through the compound Gaussian model, first using the distribution of the texture, and then in terms of the probability density function of tilting slopes (which avoids the requirement of the knowledge of the texture distribution). The results from the above two methods are in good agreement and both agree well with the Monte Carlo simulation. [ABSTRACT FROM PUBLISHER]

This paper describes a modification to a standard migration algorithm for land mine detection with a ground-penetrating radar (GPR) system. High directivity from the antenna requires a significantly large aperture in relation to the operating wavelength, but at the frequencies of operation of GPR, this would result in a large and impractical antenna. For operator convenience, most GPR antennas are small and exhibit low directivity and a wide beamwidth. This causes the GPR image to bear little resemblance to the actual target scattering centers. Migration algorithms attempt to reduce this effect by focusing the scattered energy from the source reflector and consequentially improve the target detection rate. However, problems occur due to the varying operational conditions, which result in the migration algorithm requiring vastly different calibration parameters. In order to combat this effect, this migration scheme stacks multiple versions of the same migrated data with different velocity values, whereas some other migration schemes only use a single velocity value. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

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]

In high-resolution synthetic aperture radar (SAR) images, the spatial pattern of a homogenous region conveys rich information regarding the specific scatterers being imaged. Analytical modelings of both scattering and imaging processes are critical to better interpret a specific scatterer’s appearances in SAR imagery and thereafter quantitatively retrieve its physical parameters. Small-scale rough surface scattering represents one of the mostly common scattering mechanisms and yet its SAR image characteristics have not been well studied. This becomes more critical in millimeter-wave/terahertz regime as smooth surfaces would become slightly rough under millimeter/submillimeter wavelengths. In this paper, we recast the small perturbation method (SPM) approximation of rough surface scattering under the deterministic finite-length surface condition. By ignoring evanescent waves, a simplified SPM solution for rough facet is derived as well as the analytical form of its SAR image under the conventional setup. Then, we reformulate the scattering imaging process of rough facet as a signal processing chain that further reveals the underlying mechanism of rough surface as imaged by radar. The proposed method is extensively validated against the method of moments in terms of both scattering coefficients and imaging patterns. It is found that under the conventional SPM validity condition, the error of scattering coefficients is less than 1.5 dB, while the correlation between SPM-calculated and MoM-calcualted SAR images is larger than 0.9. The proposed model of rough facet SAR image could serve as the theoretical basis for parameter inversion and surface reconstruction. Preliminary cases of the inversion of rough surface spectrum using both simulated and real SAR image patches are presented. [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 complete facet model for the backscattering from rough sea surface with breaking waves is proposed in this paper. In consideration of the spatial distribution of breaking waves on sea surface, the model is able to give a good interpretation to the “super events” under high sea states, such as sea spikes and high polarization ratios. Based on the proposed model, normalized radar cross section plots of sea surface under backscattering configuration are calculated and compared with measured data. The comparisons show that the proposed model is tractable to estimate the scattering from electrically large ocean surface under high sea states with accuracy and efficiency. In addition, the non-Gaussian statistics and spatial correlation properties of sea clutter are analyzed under different range resolutions and incident angles. The results show that high kurtosis value, which means a sea spike phenomenon, mostly happens in lower grazing angle and higher range resolution cases. The comparisons of simulated SAR images of sea surface with and without breaking waves also reveal that sea spikes and high polarization ratios are caused by breaking waves. All the simulation results prove that our model not only is able to explain the physical mechanism of the scattering but also can be applied to the analyses of statistical properties of sea clutter under high sea states. [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]

Evaluation of hydraulic fractures has been under intensive study since the last decade. Among published works, only a few have included casing effects. This paper focuses on the through-casing electromagnetic (EM) induction imaging method that evaluates hydraulic fractures with enhanced contrasts. An experimental system and an inverse scattering algorithm are presented. The laboratory scaled experimental system is built for the feasibility study of the EM induction imaging method for the through-casing hydraulic fracture evaluation. To develop the inverse scattering algorithm, fractures outside boreholes with metallic casing are modeled by a novel hybrid approximation method. This method combines the distorted Born approximation and the mixed order stabilized bi-conjugate gradient fast Fourier transform method to solve the forward scattering problem. The variational Born iterative method is applied to solve the nonlinear inverse problem iteratively. Experimental results show that the inverse scattering algorithm is effective for EM contrast enhanced through-casing hydraulic fracture evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we report results from the analysis of 3-D L-band airborne synthetic aperture radar (SAR) acquisitions acquired in March 2014 over the Mittelbergferner glacier, Austrian Alps, during the European Space Agency (ESA) campaign AlpTomoSAR. The campaign included coincident in situ measurements of snow and ice properties and ground-penetrating radar (GPR) data acquired at 600 and 200 MHz over a total length of 18 km. Radar data were acquired by repeatedly flying an L-band SAR along an oval racetrack at an altitude of about 1300 m over the glacier, such that two data stacks from opposite views are obtained. Data from all passes were coherently combined to achieve 3-D resolution capabilities, resulting in the generation of 3-D tomographic SAR (TomoSAR) cubes, where each voxel represents L-band radar reflectivity from a particular location in the 3-D space at a spatial resolution on the order of meters. TomoSAR cubes were finally corrected to account for wave propagation velocity into the ice, which was a necessary step to associate the observed features with their geometrical location, hence enabling a direct comparison to GPR data. The TomoSAR cubes show the complexity of the glacier subsurface scattering. Most areas are characterized by surface scattering in proximity of the ice surface, plus a complex pattern of in-depth volumetric scattering beneath and scattering at the ice/bedrock interface. Various subsurface features observed in GPR transects at 200 MHz clearly showed up in TomoSAR sections as well, particularly firn bodies, crevasses, layer transitions, and bedrock reflection down to 50 m below the ice surface. [ABSTRACT FROM PUBLISHER]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]