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ABSTRACTCompact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model. [ABSTRACT FROM AUTHOR]

Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility of scattering mechanism identification from limited synthetic aperture radar (SAR) data for civilian vehicles. Wide-angle 3D SAR imaging is considered with limitations in both frequency/look-angle and polarization samples. Accordingly, the two main problems, namely, sparse reconstruction of wide-angle data and identification of scattering mechanisms using two polarization channels, are jointly addressed. A methodology involving compressed-sensing (CS) imaging, processing of sub-aperture images, and application of $H/\bar {\alpha }$ decomposition to the dual-circular polarization (DCP) mode is proposed. The 2D and 3D maps of entropy $(H)$ and alpha-angle $(\bar {\alpha })$ parameters and $H/\bar {\alpha }$ classification results are evaluated by using simulation and the real GOTCHA dataset. The approach is tested with a complementary situation that consists of back-projection (BP) imaging of complete data plus decomposition of full-polarimetric (FP) data. A good correlation between full-available and most-limited cases, i.e., BP-FP vs. CS-DCP, is observed especially for the $\bar {\alpha }$ signatures. The results indicate a reasonably accurate retrieval of canonical mechanisms from a very small subset, i.e., about 0.23% of the total samples of each DCP channel.

Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provides compact polarimetric synthetic aperture radar (CP SAR) data. The complex CP data have similar properties to the complex quad polarimetric (QP) data provided by prior RADARSAT missions. In this article, a land cover classification method using spatial information is designed based on the statistical characteristics of the complex CP and QP SAR data. First, the local spatial dependency among pixels is captured by superpixels. Second, a graph is constructed on the superpixels to model the global spatial dependency among superpixels. The land cover classification image with land cover type labels is then estimated by propagating labels from the few labeled superpixels to the unlabeled superpixels. Classification of two RCM complex CP and QP scenes demonstrates that the proposed method, with few labeled pixels, provides much higher classification accuracy than methods that do not exploit global spatial dependency.

Three radar polarimetric indices, Radar Vegetation Index (RVI), Canopy Structure Index (CSI) and Radar Forest Degradation Index (RFDI)—normally associated with quad-pol (QP) synthetic aperture radar (SAR) sensors—were derived using compact polarimetry (CP) data from the Radarsat Constellation Mission (RCM). Indices were generated over a 10,000-hectare temperate mixedwood forest containing a range of complex forest structures. For comparative purposes, the same indices were generated using Radarsat-2 QP data. Agreement between CP and QP indices were assessed across broad vegetated land cover types, at the forest stand-level wherein ground plots and airborne LiDAR data were available, and at the pixel level within validation stands representing different forest types. Agreement was consistently strong for the RVI and weak for the CSI, with agreement stronger when generalized to the stand level. Indices were more informative on differences between vegetation types (e.g. forest and open wetland) than between forest types with different structures. With a radar nominal off-nadir incidence angle at 38°, RCM CP indices had narrower dynamic ranges compared to Radarsat-2 QP indices, especially the CSI, contributing to a lower level agreement for the CSI. CP data enables derivation of RVI, CSI and RFDI indices simultaneously, and provides large spatial coverage and capacity for dense time-series collection, features which are unavailable from current QP sensors. [ABSTRACT FROM AUTHOR]

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments. • Dual-polarized and compact-polarized SAR are effective for river ice monitoring. • Rubble ice, sheet ice, and open water are discriminated with 90% accuracy. • Cross-polarized backscatter aids co-polarized if the SAR noise floor is low. • Same-sense, opposite-sense, and co-polarization ratio parameters work in concert. • Model development with recursive partitioning produces robust SAR decision trees. [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

Operational sea ice maps are usually generated manually using dual-polarization (DP) synthetic aperture radar (SAR) satellite imagery, but there is strong interest in automating this process. Recently launched satellites offer compact polarimetry (CP) imagery that provides more comprehensive polarimetric information compared to DP, which compels the use of CP for automated classification of SAR sea ice imagery. Existing sea ice scene classification algorithms using CP imagery rely on handcrafted features, while neural networks offer the potential of features that are more discriminating. We have developed a new and effective sea ice classification algorithm that leverages the nature of CP data. First, a residual-based convolutional neural network (ResCNN) is implemented to classify each pixel. In parallel, an unsupervised segmentation is performed to generate regions based on CP statistical properties. Regions are assigned a single class label by majority voting using the ResCNN output. For testing, quad-polarimetric (QP) SAR sea ice scenes from the RADARSAT Constellation Mission (RCM) are used, and QP, DP, CP, and reconstructed QP modes are compared for classification accuracy, while also comparing them to other classification approaches. Using CP achieves an overall accuracy of 96.86%, which is comparable to QP (97.16%), and higher than reconstructed QP and DP data by about 2% and 10%, respectively. The implemented algorithm using CP imagery provides an improved option for automated sea ice mapping.

The RADARSAT Constellation Mission (RCM) is a Canadian Synthetic Aperture Radar (SAR) mission, providing C-band SAR data continuity of the RADARSAT-1 and RADARSAT-2 satellite missions. The RCM which was recently launched (June 2019) will provide compact polarimetry (CP) as a polarization option for all non-quad-polarization imaging modes. Maritime pollution monitoring is one of the RCM core applications. Thus, the purpose of this study is to provide an initial evaluation of the expected potential of the RCM mission for maritime pollution monitoring. Herein, we investigate simulated CP SAR data of three RCM Medium Resolution (MR) SAR modes for oil slick detection and discrimination from lookalike (LA) features. Results of our study indicated that the steeper radar incidence angle seems to be preferable for oil slick detection in all examined modes. The ScanSAR 50 m resolution (SC50M) mode showed the highest overall performance in terms of LA and Emulsion (EM) classification (95.4%). This was also valid for LA and Crude Oil (CO) classification (96.7%). The performance of the SC50M mode was slightly higher than that of the ScanSAR 30 m resolution (SC30M) mode.

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. [ABSTRACT FROM AUTHOR]

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results obtained for three high-priority applications; flood mapping, sea ice analysis, and wetland classification. In our study, the focus is on results obtained using not only linear polarization, but also the adopted Compact Polarimetric (CP) architecture in RCM. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba, suggesting smooth continuity between the two satellite missions for operational flood mapping. Visual analysis of coincident RCM CP and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. A statistical analysis using selected sea ice samples confirmed the increased contrast between thin and both rough and deformed ice in CP SAR. This finding is expected to enhance Canadian Ice Service’s (CIS) operational visual analysis of sea ice in RCM SAR imagery for ice chart production. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87. Marsh presented the highest user’s and producer’s accuracies (87.77% and 82.08%, respectively) compared to fog, fen, and swamp.

Compact Polarimetry has gained significant importance in recent years among other earth observation missions due to its low power consumption, simple architecture, and larger swath width. For space-based SAR systems, these parameters are vital to monitor the earth surface continuously for various applications. The main manifestations of Hybrid polarimetry from fully polarimetric systems is transmitting a circular polarization and receiving in linear polarizations. In this paper, we assess the performance of compact polarimetry (hybrid polarimetry) over dual-pol RISAT-1 data for land cover classification over various ground targets using backscattered coefficient values, degree of polarization, and relative phase values. In order to understand the scattering mechanism of the targets, Raney decomposition, Pseudo Three Component decomposition, m-δ and m-χ decompositions were performed on the SAR datasets. The m-χ decomposition has proven to be robust when transmitting component is not perfectly circularly polarized. The support vector machine (SVM) classifier algorithm was used to classify the datasets. Three datasets (viz. RISAT-1 hybrid-pol data, RISAT-1 dual-pol data, and Resourcesat-2 data) were evaluated with SVM classifier and compared using three different kernel parameters, i.e. radial basis function (RBF), Polynomial with degree `2' and Linear. From this paper, it was observed that the SVM with RBF kernel parameter gave highest Overall Accuracy (OA) of 92.34% for hybrid Pol RISAT-1 data. Similarly, the SVM with RBF kernel parameter gave an overall accuracy (OA) of 76.83% for dual-pol RISAT-1 data. SVM has classified the datasets into four classes viz. Urban, Water, Vegetation, and Bare soil. The evaluation of classified datasets were performed using confusion matrix for accuracy assessment. For validating the results, the classified image is compared with the optical imagery of Resourcesat-2 (LISS IV) sensor, Google Earth, and In-situ information that was collected synchronous to the satellite pass on July 5, 2016.

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 ρ = ± j under the left circular mode and ρ = ±1 under the linear π/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 ΔαB/αB 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. [ABSTRACT FROM AUTHOR]

Automatic classification methods using satellite imagery are beneficial in the sea-ice-type mapping of the Arctic regions. In the near future, the RADARSAT Constellation Mission (RCM) will be launched, providing unique compact polarimetric (CP) synthetic aperture radar (SAR) data, expected to be an improvement over the current RADARSAT-2 dual-polarimetric SAR imagery. This motivates the implementation of a CP-dedicated automatic scene classification approach. First, an existing unsupervised segmentation algorithm called iterative region growing using semantics (IRGS) is used to segment ice-class homogeneous regions to reduce the impact of speckle noise. Second, a support vector machine (SVM) is used to classify the ice-type labels for each homogeneous region. Two complex quad-polarimetric RADARSAT-2 scenes are used to mathematically simulate the corresponding CP scenes for algorithm testing. Classification accuracy shows that using only the two CP intensity images leads to improved results compared with standard dual-polarimetric scenes. Using the CP data, the best classification results are obtained with the reconstructed QP data for the IRGS segmentation and all derived CP features for the SVM labeling. The results support the expected potential that CP scenes will provide improved sea-ice classification than the current operational dual-pol scenes. [ABSTRACT FROM AUTHOR]

The RADARSAT Constellation Mission (RCM) is a Canadian Synthetic Aperture Radar (SAR) mission, providing C-band SAR data continuity of the RADARSAT-1 and RADARSAT-2 satellite missions. The RCM which was recently launched (June 2019) will provide compact polarimetry (CP) as a polarization option for all non-quad-polarization imaging modes. Maritime pollution monitoring is one of the RCM core applications. Thus, the purpose of this study is to provide an initial evaluation of the expected potential of the RCM mission for maritime pollution monitoring. Herein, we investigate simulated CP SAR data of three RCM Medium Resolution (MR) SAR modes for oil slick detection and discrimination from lookalike (LA) features. Results of our study indicated that the steeper radar incidence angle seems to be preferable for oil slick detection in all examined modes. The ScanSAR 50 m resolution (SC50M) mode showed the highest overall performance in terms of LA and Emulsion (EM) classification (95.4%). This was also valid for LA and Crude Oil (CO) classification (96.7%). The performance of the SC50M mode was slightly higher than that of the ScanSAR 30 m resolution (SC30M) mode. [ABSTRACT FROM AUTHOR]

Spaceborne Synthetic Aperture Radar (SAR) instruments are effective tools for monitoring and mapping wetlands. With the availability of SAR instruments providing various polarization options, the scope of this study is to evaluate the new compact polarization for wetland multitemporal change detection using simulated RADARSAT Constellation Mission (RCM) SAR data. A series of fully polarimetric (FP) SAR images were collected over a test site located in Ontario, Canada, and used to simulate RCM compact polarimetric (CP) data. The simulated data were evaluated for multitemporal change detection and the results were compared to those from FP SAR data. WorldView imagery and water level data were used for analysis and validation of the change detection results. The study shows potential for using the CP SAR for multitemporal change detection over three major wetland classes: shallow water, marsh, and swamp. Although FP SAR was slightly more effective in multitemporal change detection compared to CP SAR, the percentage of agreement between the change detection results of FP and CP SAR was always greater than 90% for all wetland classes. The highest overall percentage of agreement (98.4%) between the results of FP and CP SAR was observed over the shallow water, while the lowest (92.5%) was observed over swamp. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of true transmitter polarization state estimation for circular compact SAR modes. We employ two methods—the first using point targets such as trihedral reflectors, and the second, a new method, based on the Pauli phase observed over distributed targets like forest canopy. We first show how compact modes allow estimation of the Pauli phase for reflection symmetric scatterers. We show that this phase remains remarkably constant over forest canopies, depending primarily on the dielectric constant of constituent volume particles. We show that small imperfections in the transmit polarization state then lead to large errors in this phase estimate. These errors can then be used as the basis for a calibration strategy that allows estimation of a set of candidate true transmitter states. These can then be compared with the trihedral estimates for validation. We illustrate using L-band compact data from the JAXA ALOS-2 satellite and C-band data from the ISRO RISAT-1 satellite. [ABSTRACT FROM AUTHOR]

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.

Synthetic Aperture Radar (SAR) has been used in characterizing intertidal zones along northern Canadian coastlines. RADARSAT-2, with its full polarimetric information, has been considered for monitoring these vulnerable ecosystems and helping enhance the navigational safety of these waters. The RADARSAT Constellation Mission (RCM) will ensure data continuity with three identical SAR satellites orbiting together, providing superior revisit capabilities. The three satellites are equipped with multiple configurations, including single-polarization (HH, HV, VV), conventional (HH-HV, VV-VH, and HH-VV), hybrid (i.e., compact) dual polarization, and fully polarimetric (FP) modes. This study investigates the potential of the compact polarimetric (CP) mode for mapping an intertidal zone located at Tasiujaq village on the southwest shore of Ungava Bay, Quebec. Simulated RCM data were generated using FP RADARSAT-2 images collected over the study site in 2016. Commonly used tools for CP analysis include Raney m-delta classification and the hybrid dual polarizations RH-RV (where the transmitter is right-circular and the receivers are horizontal and vertical linear polarizations) and RR-RL (where the transmitter is right circular and the receivers are right-circular and left-circular polarizations). The potential of CP is compared with single, conventional dual-pol, and FP. The Freeman–Durden and Touzi discriminators are used for FP analysis. The random forest classifier is used as a classification approach due to its well-documented performance compared to other classifiers. The results suggest that the hybrid compact (RR-RL and RH-RV) dual polarizations provide encouraging separability capacities with overall accuracies of 61% and 60.7%, respectively, although they do not perform as well as conventional dual-pol HH-HV (64.4%). On the other hand, the CP polarimetric m-delta decomposition generated slightly less accurate classification results with an overall accuracy of approximately 62% compared to the FP Freeman–Durden (67.08%) and Touzi discriminators (71.1%).

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (σ°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than σ°, with correlation coefficients up to R ≃ 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of σ° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized σ°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9.

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m3/m3. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m3/m3). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular transmit (RL); and right circular transmit, right circular receive (RR)) in compact polarimetry (CP) mode. Using 256 buoy measurements collocated with RADARSAT-2 fine beam quad-polarized scenes, RCM CP data was simulated using a “CP simulator”. Provided that the relative wind direction is known, our results demonstrate that wind speed can be retrieved from RV, RH and RL polarization channels using existing C-band model (CMOD) geophysical model function (GMF) and polarization ratio (PR) models. Simulated RR-polarized radar returns have a strong linear relationship with speed and are less sensitive to relative wind direction and incidence angle. Therefore, a model is proposed for the RR-polarized synthetic aperture radar (SAR) data. Our results show that the proposed model can provide an efficient methodology for wind speed retrieval.

RADARSAT Constellation Mission (RCM) compact polarimetry (CP) data were simulated using 504 RADARSAT-2 quad-pol SAR images. These images were used to samples CP data in three RCM modes to build a data set with co-located ocean wind vector observations from in situ buoys on the West and East coasts of Canada. Wind speeds up to 18 m/s were included. CP and linear polarization parameters were related to the C-band model (CMOD) geophysical model functions CMOD-IFR2 and CMOD5n. These were evaluated for their wind retrieval potential in each RCM mode. The CP parameter Conformity was investigated to establish a data-quality threshold (>0.2), to ensure high-quality data for model validation. An accuracy analysis shows that the first Stokes vector (SV0) and the right-transmit vertical-receive backscatter (RV) parameters were as good as the VV backscatter with CMOD inversion. SV0 produced wind speed retrieval accuracies between 2.13 m/s and 2.22 m/s, depending on the RCM mode. The RCM Medium Resolution 50 m mode produced the best results. The Low Resolution 100 m and Low Noise modes provided similar results. The efficacy of SV0 and RV imparts confidence in the continuity of robust wind speed retrieval with RCM CP data. Three image-based case studies illustrate the potential for the application of CP parameters and RCM modes in operational wind retrieval systems. The results of this study provide guidance to direct research objectives once RCM is launched. The results also provide guidance for operational RCM data implementation in Canada’s National SAR winds system, which provides near-real-time wind speed estimates to operational marine forecasters and meteorologists within Environment and Climate Change Canada.

Compact polarimetry for a synthetic aperture radar (SAR) system is reviewed. Compact polarimetry (CP) is intended to provide useful polarimetric image classifications while avoiding the disadvantages of space-based quadrature-polarimetric (quad-pol) SARs. Two CP approaches are briefly described, π/4 and circular. A third form, hybrid compact polarimetry (HCP) has emerged as the preferred embodiment of compact polarimetry. HCP transmits circular polarization and receives on two orthogonal linear polarizations. When seen through its associated data processing and image classification algorithms, HPC’s heritage dates back to the Stokes parameters (1852), which are summarized and explained in plain language. Hybrid dual-polarimetric imaging radars were in the payloads of two lunar-orbiting satellites, India’s Earth-observing RISAT-1, and Japan’s ALOS-2. In lunar or planetary orbit, a satellite equipped with an HCP imaging radar delivers the same class of polarimetric information as Earth-based radar astronomy. In stark contrast to quad-pol, compact polarimetry is compatible with wide swath modes of a SAR, including ScanSAR. All operational modes of the SARs aboard Canada’s three-satellite Radarsat Constellation Mission (RCM) are hybrid dual-polarimetric. Image classification methodologies for HCP data are reviewed, two of which introduce errors for reasons explained. Their use is discouraged. An alternative and recommended group of methodologies yields reliable results, illustrated by polarimetrically classified images. A survey over numerous quantitative studies demonstrates HCP polarimetric classification effectiveness. The results verify that the performance accuracy of the HCP architecture is comparable to the accuracy delivered by a quadrature-polarized SAR. Four appendices are included covering related topics, including comments on inflight calibration of an HCP radar.

Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the H/ᾱ plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) has proven to be sensitive to different land parameters, such as vegetation or soil moisture. In this manuscript, the correlation of the Stokes parameters of the GNSS reflections collected by the Soil Moisture Active Passive - Reflectometry (SMAP-R) with SMAP soil moisture (SM), SMAP vegetation optical depth (VOD), and the Global Forest Canopy Height (CH) are analyzed. SMAP-R is the first hybrid compact polarimetric reflectometer in space, and it provides a valuable dataset useful to design future GNSS-R mission that target polarimetric retrievals. The SMAP-R total-intensity reflectivity is presented, estimated from the bistatic radar cross section assuming coherent reflection. The calibrated intensity and the normalized Stokes parameters of the received wave are compared for a 1-year period to the SMAP VOD, the SMAP SM, the Global Forest CH, and the standard deviation of the digital elevation model. To provide a first assessment of the performance of full-Polarimetric GNSS-R to estimate SM and VOD, a linear regression approach is implemented using the four Stokes parameters and the child Stokes parameters. The two approaches show a correlation coefficient of R = 0.73 and R = 0.55 to estimate SM and VOD, respectively, enabling the estimation of both variables using polarimetric GNSS-R data without the need for ancillary data. Finally, the differences between the polarimetric signature at different incidence angles and polarizations are discussed. • GNSS-R Stokes parameters allow for a full polarimetric forward scattering view. • The Stokes parameters can be used for independent soil moisture retrieval. • H/V ratios correlate significantly to soil moisture at 40° of incidence angle. • The third Stokes parameter (45° component) is sensitive to dense vegetation. [ABSTRACT FROM AUTHOR]

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%).

This study investigates the different performances between Compact-Polarimetric (CP) and Full-Polarimetric (FP) SAR to retrieve multiple crop growth parameters including Vegetation Water Content (VWC), Leaf Area Index (LAI), height, and dry biomass. The objective is to study at which conditions the CP SAR can obtain comparable retrieval accuracy as FP SAR for specific crop types and growth descriptors. Polarimetric decompositions were used to extract CP and FP explanatory variables to quantify crop growth status. Then, the sensitivities of CP and FP variables to different crop descriptors were analyzed, revealing higher sensitivity to height than LAI, VWC, and dry biomass. In order to reduce the information redundancy of sets of CP and FP SAR variables, Partial Least Square (PLS) approach was used to develop new orthogonal SAR parameters, while Step-Wise Regression (SWR) was used to determine the optimal SAR parameters, followed by retrievals of multiple crop growth descriptors using the developed estimators. Validated by the ground measurements, the retrieval performances are found to be highly dependent on polarimetry dimension (CP or FP), crop types, and targeted crop descriptors. With crop growth and enhanced depolarization effects, the CP SAR can capture the increasing volume power and decreasing surface power but at a lower rate than FP SAR. The m - δ and m - χ decompositions provide similar scattering components which differ from the Random Volume Over Ground (RVOG) decomposition. The third Stokes parameter g 3 of the CP SAR data that indicates the strength of the circular polarization component is a common important parameter to infer the multiple crop parameters of interest. In agreement with the sensitivity analysis, better retrieval accuracies were obtained for height and dry biomass than VWC and LAI. For short vegetation such as soybean, full polarimetry is required to obtain the estimates of all four crop growth descriptors. On the contrary, for tall crop growth parameters, such as canola (height and dry biomass), corn (height, VWC, dry biomass), and wheat (LAI, VWC, dry biomass), the CP SAR features can provide comparable robust retrieval accuracy as the FP SAR features. This study deepens our insights into the CP SAR of the RADARSAT Constellation Mission (RCM) to timely monitor crop growth and health status. • Analyze different retrieval performances of Compact- and Full-Polarimetric SAR. • m - δ , m - χ , and RVOG decompositions were evaluated to monitor crop growth. • Third stokes parameter g 3 was effective to estimate multiple crop parameters. • Retrieval of crop height was better than LAI, biomass, and vegetation water content. • CP and FP SAR were suitable for tall (canola, corn, and wheat) and short crops (soybean), respectively. [ABSTRACT FROM AUTHOR]

The C-band synthetic aperture radar (SAR) data from the Bohai Sea of China, the Labrador Sea in the Arctic and the Weddell Sea in the Antarctic are used to analyze and discuss the sea ice full polarimetric information reconstruction ability under compact polarimetric modes. The type of compact polarimetric mode which has the highest reconstructed accuracy is analyzed, along with the performance impact of the reconstructed pseudo quad-pol SAR data on the sea ice detection and sea ice classification. According to the assessment and analysis, it is recommended to adopt the CTLR mode for reconstructing the polarimetric parameters σ , σ , H and α, while for reconstructing the polarimetric parameters σ , ρ , λ and λ , it is recommended to use the π/4 mode. Moreover, it is recommended to use the π/4 mode in studying the action effects between the electromagnetic waves and sea ice, but it is recommended to use the CTLR mode for studying the sea ice classification. [ABSTRACT FROM AUTHOR]

We have investigated the use of C-band compact polarimetric synthetic aperture radar for estimation of ocean surface wind speeds. Using 1399 buoy observations collocated with Radarsat-2 scenes, compact polarimetric data was simulated for two of the Radarsat Constellation’s planned wide swath imaging modes. Provided the wind direction is known or can be estimated, our results demonstrate that wind speed can be estimated from the right-vertical polarization channel of the compact polarimetry using a combination of the CMOD5 geophysical model function and a linear model. If wind speed estimation without wind direction input is desired, the randomly-polarized component of the backscattered power can be used in a similar fashion to that of the linear cross-polarizations, but is less affected by increases in the noise effective sigma-zero of the data. A model is proposed for the randomly-polarized power as a function of incidence angle and wind speed, independent of wind direction. The results suggest that compact polarimetry is a strong alternative to linearly polarized synthetic aperture radar data for wind speed estimation applications, particularly for wide swath imaging modes with a high noise floor. [ABSTRACT FROM AUTHOR]

A new model has been developed to estimate the dielectric permittivity of the lunar surface regolith using S-band hybrid compact polarimetric SAR data obtained from Mini-RF aboard LRO. The surface regolith is modeled as a random medium consisting of elementary ellipsoidal particles smaller than the incident wavelength of S-band. The data, available in the form of Stokes vector, are used to derive a coherency matrix, under the reflection symmetry condition, whose elements are used to calculate the particle anisotropy parameter. Since the anisotropy is bounded by the dielectric permittivity, its relationship with the latter is used for the required estimation. The method is applied to compute the dielectric permittivity of Apollo 17 landing site in Taurus-Littrow valley and to a part of Sinus Iridum. The estimated mean dielectric permittivity values ( 2.87 ± 0.31 ) and ( 3.04 ± 0.31 ), respectively, are consistent with the previous estimates. The dielectric permittivity values have also been used to discern different units of regolith in both the regions. The advantage of our model is that it does not require any a priori knowledge about the density or composition of the regolith. The available data in the form of Stokes parameters are sufficient for the computation. The model predicts a thin layer of low density, porous fine grained dust on the lunar surface. [ABSTRACT FROM AUTHOR]

Cloude–Pottier entropy and α-angle are two important parameters for the interpretation of fully polarimetric data. They indicate the randomness of the polarisation of the back scattered waves and the scattering mechanisms of the targets respectively. For fully polarimetric data the H-α plane is presented which using the borders of it the full polarimetric data can be classified into 8 different physical scattering mechanisms. In recent years new approaches have proposed H-α classification spaces by mapping the points which are belong to each PSMs of FP data into the space of H/α for CP data and approximate borders were extracted for the classification purpose. In this paper a novel approach for defining H/α classification plane has been presented which maximizes the producer’s accuracy. The optimum borders have been found and the results of classification using the new plane have been compared with the rival method and the superiority of the new proposed method has been revealed. [ABSTRACT FROM AUTHOR]

An analysis of the polarimetric scattering properties of oil-covered waters is conducted using the classic Poincaré ellipticity parameter chi (\bm \chi ) and the degree of polarization (\bm m) from the Stokes parameters of hybrid polarized mode synthetic aperture radar (SAR). Oil spills reduce \bm m for all four cases considered in this study. However, for the natural oil seep case considered, \bm \chi has a change in signs, comparing oil-covered waters with the “clean” ocean surface. The \bm \chi sign reversal is basic for “sign difference oil spill detection methods.” However, a problem is that the oil spill related to the deep water horizon (DWH) disaster did not exhibit a reversal in \bm \chi signs, comparing the “clean” ocean surface to the area contaminated by crude oil. The scattering mechanism related to the oil seep is different from that of the DWH oil spill; the former is dominated with even bounce scattering and the latter is dominated by Bragg scattering, similar to that of the clean oil-free ocean surface, in the imaging area. [ABSTRACT FROM PUBLISHER]

Ocean surveillance is one of the important applications in synthetic aperture radar (SAR) imagery. Polarimetric SAR provides multichannel information and shows great potential for ocean target observation. Oil-spills and ships possess different polarimetric features from the ocean surface, whose physical backscattering property is generally admitted as being dominated by the Bragg resonant scattering. In this study, we focus on the oil-spill and ship observation based on the polarimetric features. Oil-spills present a non-Bragg scattering property. While backscatter from ships is predominated by the double-bounce scattering and the multiple reflections between the ship and sea surface. Ships exhibit very different scattering characteristics from the ocean Bragg scattering. Based on the extended Bragg (X-Bragg) model, a new method is proposed for observing oil-spills and ships. This method allows distinguishing oil-spills from two kinds of important look-alikes, i.e., biogenic slicks and the low-wind region (LWR), and also shows good performance for ship enhancement. Experiments are performed on the C-band fully polarimetric SAR data acquired by both SIR-C/X-SAR and RADARSAT-2. The other issue concerned is the application potential of the hybrid dual-polarimetric (i.e., compact polarimetry (CP)) SAR mode, which has the advantage of providing larger imaging coverage compared to the full polarimetry. Based on the X-Bragg model, where the backscatter reflection symmetry is assumed, an equivalent method is proposed with the circularly polarized transmission CP mode. Experimental results show that similar results can be obtained between the CP and full polarimetry for ocean target observation. [ABSTRACT FROM PUBLISHER]

A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data. [ABSTRACT FROM PUBLISHER]

Iceberg monitoring is an important application of synthetic aperture radar (SAR), and is one of the stated objectives of the Radarsat Constellation, the next generation of Canada's Radarsat satellites. In this paper, we simulate Radarsat Constellation data in a number of different imaging modes, using Radarsat-2 single-look complex data covering a study area in the Labrador Sea. We test the iceberg detection performance of both linear dual-pol data as well as compact polarimetry, a novel SAR architecture that transmits circular polarization rather than the traditional horizontal or vertical polarizations. We use the likelihood ratio test method to calculate a decision variable image for each of a number of different dual-pol and compact configurations, then analyze the detection performance using 25 validated iceberg locations spread across 12 different scenes. We found that compared to the linear data, the compact data missed fewer targets, and detected a greater number of pixels of detected targets, for most of the incidence angles and imaging modes tested. Compact polarimetry seems to be a promising choice for iceberg detection applications. [ABSTRACT FROM AUTHOR]

An improved reconstruction algorithm is proposed for compact polarimetric (CP) synthetic aperture radar (SAR) on oil spill detection. Based on the differences in statistical behavior between open and oil-covered sea surfaces, the proposed algorithm can iteratively reconstruct quad-pol SAR images from CP SAR data. During the experiment, it outperformed two existing compact SAR reconstruction algorithms in terms of both statistical and information theoretical analysis. [ABSTRACT FROM PUBLISHER]

Dual and compact polarimetric (CP) synthetic aperture radar (SAR) provide more target information than single-polarization SAR systems with less stringent data requirements than fully polarimetric SAR systems. Considering the incomplete nature of CP data to establish the quad or fully polarimetric scattering matrix, the applied CP mode determines the captured scattering properties. Present formalism enables investigation of various CP modes (e.g., general elliptical transmission) and products for general target backscattering characterization. This is done through decomposition of quad-pol covariance and CP modeling of respective basic scattering mechanisms. On this basis, derivation of optimal CP modes and design of suitable CP products are explored. In this approach, the need for a priori target assumptions is removed. The link established between general CP and quad polarimetric covariance matrices can be used to examine target property assumptions using various CP modes data. Accordingly, means is also provided to explore a priori target property assumptions and their suitability required for expansion of 2 × 2 CP covariance matrix to perform the pseudo-quad-polarimetric covariance analysis. [ABSTRACT FROM AUTHOR]

Abstract: Quad-pol data are generally acknowledged as providing the highest performance in ship detection applications using SAR data. Yet quad-pol data have half the swath width of single and dual-pol data and are thus less useful for maritime surveillance, where wide area coverage is crucial. Compact polarimetry (CP) has been proposed as a compromise between swath width and polarization information. The circular-transmit-linear-receive (CTLR) CP data have certain engineering advantages over other CP configurations. CP data may be used to reconstruct a reduced quad-pol covariance matrix (termed pseudo-quad, or PQ, data) and the potential of these data in terrestrial applications has recently been demonstrated. We present some of the first results on the use of CTLR data and reconstructed quad-pol data for ship detection. We use Radarsat-2 fine-quad (FQ) data to examine 76 ships over a range of incidence angles and ship orientations at low to moderate wind speeds. We examined the ship detection performance of full quad-pol and full-PQ data; several dual-pol configurations suggested in the literature, HV and PQ HV and the raw CTLR data. We find that the ship detection performance of the PQ HV data is the strongest of all the detectors we examined, with performance that was comparable to quad-pol data. Other strong performers were HV and CTLR data. [Copyright &y& Elsevier]

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry, hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes. We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean. [ABSTRACT FROM PUBLISHER]

In recent years, the use of synthetic aperture radar (SAR) for iceberg detection has been increasing thanks to the greater availability and coverage of SAR data, particularly polarimetric data. Greater amounts of polarimetric information can increase detection performance, preventing false alarms and missed detections. However, quad-polarization (quad-pol) SAR systems have increased data rate and power usage requirements, causing quad-pol modes to have generally half the swath width of dual-polarization (dual-pol) modes. Compact polarimetry is a compromise that allows the approximation of quad-pol information (referred to as “pseudo quad-pol”) using a dual-pol SAR. In this paper, using compact polarimetric data simulated from RADARSAT-2 quad-pol imagery, we show how pseudo-quad-pol data can be used for iceberg detection using the likelihood ratio test method. We show that use of the pseudo-quad-pol HV intensity can augment the detection performance of a compact polarimetric SAR system. We also compare the performance of the various compact polarimetric configurations to linearly polarized dual-pol and quad-pol data. [Traduit par la rédaction] Ces dernières années, le radar à synthèse d'ouverture (RSO) a été de plus en plus utilisé pour la détection des icebergs grâce à une meilleure disponibilité et une meilleure couverture des données RSO, en particulier des données polarimétriques. De plus grandes quantités d'informations polarimétiques peuvent améliorer la performance de détection, ce qui évite les fausses alarmes et les détections manquées. Cependant, les systèmes RSO à quadruple polarisation (quad-pol) exigent des débits binaires et une consommation d’énergie plus élevés, de telle sorte que les modes quad-pol utilisent généralement une largeur de couloir la moitié moindre que celle des modes à double polarisation (dual-pol). La polarimétrie compacte est un compromis qui permet d'approximer l'information quad-pol (alors appelée « pseudo-quad-pol ») au moyen d'un RSO dual-pol. Dans cet article, en utilisant des données de polarimétrie compacte simulées à partir de l'imagerie RADARSAT-2 quad-pol, nous montrons comment les données pseudo-quad-pol peuvent servir à la détection des icebergs au moyen de la méthode du test du rapport des vraisemblances. Nous montrons que l'utilisation de l'intensité HV pseudo-quad-pol peut augmenter la performance de détection d'un système RSO polarimétrique compact. Nous comparons aussi la performance des diverses configurations polarimétriques compactes aux données dual-pol et quad-pol linéairement polarisées. [ABSTRACT FROM AUTHOR]