Compact Polarimetric Synthetic Aperture Radar Research Articles

Compact Polarimetry Response to Modeled Fast Sea Ice Thickness

Compact Polarimetric (CP) Synthetic Aperture Radar (SAR) is expected to gain more and more ground for Earth observation applications in the coming years. This comes in light of the recently launched RADARSAT Constellation Mission (RCM), which uniquely provides CP SAR imagery in operational mode. In this study, we present observations about the sensitivity of CP SAR imagery to thickness of thermodynamically-grown fast sea ice during early ice growth (September–December 2017) in the Resolute Bay area, Canadian Central Arctic. Fast ice is most suitable to use for this preliminary study since it exhibits only thermodynamic growth in absence of ice mobility and deformation. Results reveal that ice thickness up to 30 cm can be retrieved using several CP parameters from the tested set. This ice thickness corresponds to the thickness of young ice. We found the surface scattering mechanism to be dominant during the early ice growth, exposing an increasing tendency up to 30 cm thickness with a correlation coefficient with the thickness equal to 0.86. The degree of polarization was found to be the parameter with the highest correlation up to 0.95. While thickness retrieval within the same range is also possible using parameters from Full Polarimetric (FP) SAR parameters as shown in previous studies, the advantage of using CP SAR mode is the much larger swath coverage, which is an operational requirement.

Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.

Oil Spill Discrimination by Using General Compact Polarimetric SAR Features

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric synthetic aperture radar (SAR) data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. As such, the received wave information depends on the received antenna configuration. Additionally, a polarimetric descriptor that is independent of the received antenna configuration might provide additional information which is missed by the individual elements of the coherency matrix. This implies that existing target characterization techniques might neglect this information. In this regard, we suitably utilize the 2-D and 3-D Barakat degree of polarization which is independent of the received antenna configuration to obtain distinct polarimetric information for target characterization. In this study, we introduce new roll-invariant scattering-type parameters for both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. These new parameters jointly use the information of the 2-D and 3-D Barakat degree of polarization and the elements of the coherency (or covariance) matrix. We use these new scattering-type parameters, which provide equivalent information as the Cloude α for FP SAR data and the ellipticity parameter χ for CP SAR data, to characterize various targets adequately. Additionally, we appropriately utilize these new scattering-type parameters to obtain unique non-model-based three-component scattering power decomposition techniques. We obtain the even-bounce, and the odd-bounce scattering powers by modulating the total polarized power by a proper geometrical factor derived using the new scattering-type parameters for FP and CP SAR data. The diffused scattering power is obtained as the depolarized fraction of the total power. Moreover, due to the nature of its formulation, the decomposition scattering powers are non-negative and roll-invariant while the total power is conserved. The proposed method is both qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

A Novel Statistical Texture Feature for SAR Building Damage Assessment in Different Polarization Modes

Texture features are important characteristics in distinguishing collapsed buildings and intact buildings. However, texture features currently used in synthetic aperture radar (SAR) building damage assessment are extracted following the methods of optical images directly, which do not consider the statistical feature of speckles and limit the accuracy improving. Therefore, a statistical texture feature—G0-para—was proposed to reflect the homogeneity of buildings in complex urban areas after a disaster. The G0-para is arising from the G0 distribution of SAR image and used to distinguish collapse buildings and intact buildings. First, the G0-para is unified to satisfy different polarization data—single-/dual-/quad-/compact-polarization. Second, the distinguishing ability of G0-para is under comparison in single-/dual-/quad-/compact-polarization, through the receiver operating characteristic (ROC) curve and the area under the ROC curve analysis. Then, collapsed buildings with RADARSAT-2 and ALOS-1 data are evaluated, selecting the optimal combinations of each mode and comparing with the preferable existing texture features. The results show that the statistical texture parameter—G0-para—is better than the variance of gray-level histogram and the contrast of gray level co-occurrence matrix in distinguishing intact buildings and collapsed ones and G0-para can be applied to single-/dual-/quad-/compact-polarimetric SAR data. For experimental data, VV and HH in single polarization, VH/VV and HH/HV in dual polarization, and hybrid mode in compact polarization are recommended when the best quad polarization is unavailable.

Formalism of Compact Polarimetric Descriptors and Extension of the $\Delta\alpha_{\text{B}}/\alpha_{\text{B}}$ Method for General Compact-Pol SAR

Compact polarimetry is a dual-polarization hybrid imaging mode and provides partially backscatter characteristics of scatterers. The compact polarimetric (CP) synthetic aperture radar (SAR) measures the combinations of fully polarimetric (FP) scattering coefficients, which is dependent on the transmit-wave polarization state. The backscattered wave is fully described by a 2-D complex scattering vector, in which the dual-channel polarization ratio represents the vector nature for describing target polarimetric properties. However, due to the dependence of CP measurements on the transmit wave, the complex channel ratio is explained differently for the same scatterers under different observation modes, e.g., the channel ratio $\rho =\pm j$ under the left circular mode and $\rho =\pm 1$ under the linear $\pi /4$ mode for the canonical trihedral and dihedral scatterers. The explanation diversity is inconvenient for the use of CP data, resulting in nonunified target decomposition algorithms. In this study, first, we propose a new formalism method for the general CP SAR mode, such that all CP mode observables are described based on the same standard for target scattering characterization. This provides potential for developing unified CP decomposition algorithms. Then, based on this formalism, a polarization ratio-based scattering characterization method is proposed, which is mathematically equal to the FP $\Delta \alpha _{\text {B}}/\alpha _{\text {B}}$ method but without any scattering assumption. Theoretical analyses and discussions are provided. Experiments show that the proposed method is efficient for compact polarimetric scattering interpretation.

Least-Squares Estimation for Pseudo Quad-Pol Image Reconstruction from Linear Compact Polarimetric SAR

Compact polarimetry is a hybrid dual-polarization imaging mode, which is used to enable wide swath coverages and provide more polarimetric information compared with the conventional dual-polarimetric imaging modes (HH/HV and VH/VV). In applications of compact polarimetric synthetic aperture radar (Pol-SAR), pseudo quad-polarimetric (quad-pol) image reconstruction is an important technique. In this study, we propose a least-squares (LS) based method to estimate the quad-pol covariance elements for the linear π/4 compact polarimetric mode. Different from existing quad-pol reconstruction approaches, which use an iterative approach to refine the model solution based on multi-look data, the LS method uses a set of data points to best fit the reconstruction model and is applicable to both multi-look and single-look complex data. In this study, a decomposition-based three-component reconstruction model is exploited to construct the system of non-linear equations. Then, the minimization problem is addressed in a local window for the cross-polarized term, which is optimized with bound constraints. Furthermore, the $m - {\alpha _s}$ decomposition for the linear compact mode is developed, which is used to approximate the reconstruction model parameter for the LS model function. Experiments are performed on C-band RADARSAT-2 data collected over agriculture fields, an urban area, and an area with complex terrain types. In comparison with the iterative-based methods, the LS-based reconstruction method shows its superiority in estimating both the cross-polarized term and the co-polarized phase difference.

CFAR Ship Detection Methods Using Compact Polarimetric SAR in a K-Wishart Distribution

Compact polarimetric target detection is currently attracting increasing interest due to the generation of synthetic aperture radar (SAR) systems. Recently, the polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in both full-polarization and compact polarization-mode SAR images. In this paper, the probability density function (PDF) and the probability of false alarms (PFA) of the PNF are derived in the K -Wishart case (where the texture obeys the gamma distribution) since K distributions are suitable for many scenes including sea clutter. The PDF and PFA are also derived in closed forms for the polarimetric whitening filter (PWF) in the K -Wishart distribution case, the performance of which is close to that of the optimal polarimetric detector. The constant false alarm rate threshold for ship detection can be obtained from the PFA via the bisection method, which allows for the automatic and adaptive implementation of ship detection in complicated sea backgrounds in practical applications. The performances of the two detectors are compared via simulated data and measured data from AIRSAR in the compact polarimetric SAR case. The results show that the detectors given are accurate, while the PWF detector is generally better than the PNF detector in the K -Wishart case of the CP SAR model.

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization.

Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR

Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in the full-polarimetric (FP), compact-polarimetric (CP), and dual-polarimetric (DP) modes. These features were comprehensively quantified and evaluated using the Euclidean distance and mutual information, and the result indicated that the features in CP SAR are better than those of FP or DP SAR in general. The CP SAR features are thus further studied, and a new feature, named phase factor, in CP SAR mode is presented that can distinguish ships and the sea surface by the constant 0 without complex calculation. Furthermore, the phase factor is independent of the sea surface roughness, and hence it performs stably for ship detection even in high sea states. Experiments demonstrated that the ship detection performance of the phase factor detector is better than that of roundness, delta, HESA and CFAR detectors in low, medium and high sea states.

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

A contextual-based segmentation of compact PolSAR images using Markov Random Field (MRF) model

ABSTRACTAs the first major step in each object-oriented feature extraction approach, segmentation plays an essential role as a preliminary step towards further and higher levels of image processing. The primary objective of this paper is to illustrate the potential of Polarimetric Synthetic Aperture Radar (PolSAR) features extracted from Compact Polarimetry (CP) SAR data for image segmentation using Markov Random Field (MRF). The proposed method takes advantage of both spectral and spatial information to segment the CP SAR data. In the first step of the proposed method, k-means clustering was applied to over-segment the image using the appropriate features optimally selected using Genetic Algorithm (GA). As a similarity criterion in each cluster, a probabilistic distance was used for an agglomerative hierarchical merging of small clusters into an appropriate number of larger clusters. In the agglomerative clustering approach, the estimation of the appropriate number of clusters using the data log-likelihood algorithm differs depending on the distance criterion used in the algorithm. In particular, the Wishart Chernoff distance which is independent of samples (pixels) tends to provide a higher appropriate number of clusters compared to the Wishart test statistic distance. This is because the Wishart Chernoff distance preserves detailed data information corresponding to small clusters. The probabilistic distance used in this study is Wishart Chernoff distance which evaluates the similarity of clusters by measuring the distance between their complex Wishart probability density functions. The output of this step, as the initial segmentation of the image, is applied to a Markov Random Field model to improve the final segmentation using vicinity information. The method combines Wishart clustering and enhanced initial clusters in order to access the posterior MRF energy function. The contextual image classifier adopts the Iterated Conditional Mode (ICM) approach to converge to a local minimum and represent a good trade-off between segmentation accuracy and computation burden. The results showed that the PolSAR features extracted from CP mode can provide an acceptable overall accuracy in segmentation when compared to the full polarimetry (FP) and Dual Polarimetry (DP) data. Moreover, the results indicated that the proposed algorithm is superior to the existing image segmentation techniques in terms of segmentation accuracy.

Adaptive Ship Detection in Hybrid-Polarimetric SAR Images Based on the Power–Entropy Decomposition

Based on its advantages, compact polarimetric (CP) synthetic aperture radar (SAR) is considered to be a good option for earth observations. This paper proposes an adaptive method of ship detection in CP SAR images operating in the hybrid-polarimetric (HP) mode. First, according to the analysis of scattering between ships and sea background, a novel decomposition approach, named power–entropy (PE) decomposition, is developed. Based on this approach, two components of the scattering power, the high-entropy scattering amplitude (HESA) and low-entropy scattering amplitude, are separated. We demonstrate that the HESA component is an effective physical quantity indicating the difference between the ship and its background and hence can potentially be used for ship detection using HP SAR data. The generalized Gamma distribution ( $\text{G}\Gamma \text{D}$ ) is found suitable for the characterization of HESA statistics of sea clutter with a wide range of homogeneity. As a result, the adaptive constant false-alarm rate (CFAR) technique based on the HESA detector is proposed. Experiments performed using HP measurements emulated from L-band ALOS-PALSAR and C-band RADARSAT-2 full polarimetric data validate the soundness and superiority of the proposed CFAR method based on the HESA detector. Both the theoretical proof and experimental results show that the HESA improves the ship–sea contrast (or the signal–clutter ratio) more than popular detectors, such as the entropy and SPAN. Moreover, the $\text{G}\Gamma \text{D}$ is a versatile model for the description of the statistical behavior of both the HESA and comparable detectors.

Ship Detection Using Compact Polarimetric SAR Based on the Notch Filter

Compact polarimetric data exploitation, especially in hybrid-polarimetric (HP) mode, is currently attracting increasing interest due to the new generation of synthetic aperture radar (SAR) systems. Recently, it has been demonstrated that the notch filter is useful for ship detection in either full- or dual-polarization (DP)-mode SAR images. In this paper, the notch filter investigation is further extended to HP SAR architecture for ship detection on the ocean surface. First, a version of the notch filter that is suitable for HP SAR is proposed based on the definition of the corresponding feature partial scattering vector from the covariance matrix of the HP SAR. Subsequently, a novel model characterizing the statistics of the notch distance of sea clutter in the HP mode is developed. Based on the statistical model, the threshold of constant false-alarm rate (CFAR) ship detection is theoretically and analytically derived, which allows the automatic and adaptive implementation for ship detection in varying sea backgrounds in practical applications. Experiments on the HP SAR data emulated from full-polarization L-band Aerospace Exploration Agency Advanced Land Observation Satellite Phased-Array type L-band SAR and C-band RADARSAT-2 SAR measurements validate not only the soundness of the proposed CFAR detection but also the high accuracy of the presented model in fitting HP SAR data. Furthermore, the notch filter and its CFAR realization provide the same benchmark for the comparison of the detectability of HP and conventional linear DP SAR data. Preliminary findings suggest that the detection performance of HP SAR is superior to that of DP SAR in ship observation. Therefore, the proposed CFAR method based on the notch filter provides a promising technique for the detection of ships using HP SAR data.

Compact Polarimetric SAR Interferometry Target Decomposition With the Freeman–Durden Method

In this paper, we apply the Freeman–Durden decomposition method to the compact polarimetric synthetic aperture radar (SAR) interferometry (C-PolInSAR) data. The complex cross-correlation matrix of the single-baseline C-PolInSAR is decomposed into three $2\times 2$ covariance matrices, which correspond to the surface, the dihedral and the volume scattering, respectively. Under reasonable assumptions, each model of the three scattering mechanisms are derived and formulated in term of the $\pi /4$ C-PolInSAR mode and the circular polarization transmitting and linear polarization reception (CTLR) C-PolInSAR mode. Prior to the target decomposition, detailed steps to retrieve the volume scattering amplitude are given, which are remarkably different for the $\pi /4$ mode and the CTLR mode. In addition, the topographic phase is also retrieved based on the random volume over ground (RVoG) model with the C-PolInSAR data. Finally, a numerical method is applied to solve the system of nonlinear equations in the target decomposition procedure, and the amplitude contributions, as well as the vertical phase center heights are retrieved for each of the three scattering mechanisms with the C-PolInSAR data. The proposed target decomposition method has been verified both with a simulated forest data, and with the L -band BioSAR2008 airborne data acquired by the DLR E-SAR system of a forest site in the north of Vindeln municipality, Sweden, wherein detailed performance results for the proposed method are presented as compared to the more usual quad polarimetric SAR interferometry target decomposition.

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACTIn this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter αs. In addition to the parameter αs from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters αs and χ for εs > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.

Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization

Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%).

Compact Polarimetric Synthetic Aperture Radar for Marine Oil Platform and Slick Detection

Compact polarimetric (CP) synthetic aperture radar (SAR) can provide ocean surface observations with large-coverage swath and abundant polarimetric scattering information. These distinctive characteristics make CP SAR a potential tool for operational monitoring oil slicks and oil platforms, overcoming the shortcomings of small spatial coverage by the traditional quad-polarization (quad-pol) SAR. In this paper, we use the RADARSAT-2 C-band quad-pol SAR data to generate CP covariance matrix elements and subsequently construct pseudoquad-pol backscatter coefficients, using two CP reconstruction algorithms to evaluate CP SAR’s applications in detection of oil slicks and oil platforms. The reconstructed co- and cross-polarization data show good agreement with original radar observations acquired at different incidence angles and wind speeds. Furthermore, we develop an unsupervised classification method using the relative phase, a logical scalar threshold that separates odd and multiple scattering events, as an indicator to discriminate oil slicks and platforms from clean ocean waters. The relative phase is positive for clean ocean surfaces where odd scattering is dominant, but negative for oil platforms and oil slick-covered areas associated with multiple scattering. The detections of oil spills and oil platforms are validated against known oil platform geographic positions and optical aircraft surveys of the oil slicks. The proposed method provides a promising technique to detect oil slicks and oil platforms from CP imaging mode SAR data, i.e., as may be acquired by the RISAT-1, ALOS-2, and the future RADARSAT Constellation Mission.

Polarimetric Analysis of Compact-Polarimetry SAR Architectures for Sea Oil Slick Observation

In this paper, a theoretical and experimental analysis of polarimetric synthetic aperture radar (SAR) architectures is undertaken for sea oil slick observation purposes. Reference is made to the conventional full-polarimetric (FP) SAR that is here contrasted with new-generation polarimetric SAR architectures, known as compact-polarimetric (CP) SAR. Two CP modes are considered, i.e., the hybrid-polarity and π/4 modes, whose measurements are emulated from actual L- and C-band FP SAR data. Polarimetric sea surface scattering is predicted according to an extended version of the Bragg scattering model (X-Bragg) in order to point out the differences exhibited between FP and CP SAR architectures and among CP SAR modes. Theoretical predictions are then contrasted with experiments undertaken on actual polarimetric SAR data collected over well-known oil slicks and weak-damping surfactants. Results confirm model prediction, showing that differences mainly apply when polarimetric features are estimated over slick-free sea surface using different SAR architectures, with the π/4 mode behaving closer to FP SAR. Although CP SAR architectures measure only a subset of the FP information content, they represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants.

Analysis of Full and Compact Polarimetric SAR Features Over the Sea Surface

In this letter, fully polarimetric (FP) and compact polarimetric (CP) synthetic aperture radar (SAR) features are investigated over the sea surface. While exhibiting similar trends, subtle differences are observed and investigated here using a new model-based approach. The model indicates that differences between the FP and CP architectures, and among the CP modes, are due to the difference in mapping between the polarimetric observables and the eigenvalues. This is verified using actual FP SAR data and emulated CP SAR measurements.