Compact Polarimetry Synthetic Aperture Radar Research Articles

Pseudo Quad-Pol Simulation From Compact Polarimetric SAR Data via a Complex-Valued Dual-Branch Convolutional Neural Network

Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual polarization imaging mode. CP synthetic aperture radar (SAR) has a larger swath and can provide more polarimetric information compared with the traditional dual polarization imaging mode (HH/HV or VH/VV). Pseudo quad-polarimetric (quad-pol) data simulation is an important technology in the application of CP data. The goal of pseudo quad-pol data simulation from CP data is to change the form of CP data to the form of quad-pol data without increasing any new information. In this work, a new pseudo quadpol data simulation method from the CP data is proposed. This method combines a complex-valued dual-branch convolutional neural network (CV-DBCNN) to achieve the simulation of the pseudo quad-pol data. It utilizes complex-valued convolutional layers and complex-valued activation function to fully extract the polarimetric information embedded in the complex-valued CP data. For the CV-DBCNN, the branch with 11 kernel size is used to nonlinearly and self-adaptively combine the channel of input data, the branch with 33 kernel size is used to extract the discriminative regional polarimetric features. Furthermore, polarimetric decomposition is utilized to evaluate the scattering mechanisms of the pseudo simulated quad-pol data. Three state-of-the-art methods are utilized for comparison. In comparison with other methods, our proposed reconstruction method based on the CVDBCNN shows its superiority in terms of the pseudo quad-pol data reconstruction and scattering mechanism preservation.

CP-IRGS: A Region-Based Segmentation of Multilook Complex Compact Polarimetric SAR Data

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

Quad-pol reconstruction from compact polarimetry using a fully convolutional network

ABSTRACTThe compact polarimetry (CP) mode of synthetic aperture radar (SAR) has attracted significant attention in recent years due to its lower energy budget, reduced data volume and larger swath width. In this paper, the possibility and practicability of a fully convolutional network (FCN) for quad-pol reconstruction from CP SAR data are explored. A 5-Dimension normalized input vector is designed to fully represent the CP covariance matrix. The proposed FCN consists of 11 feature extraction layers and one regression layer. Among them, 11 feature extraction layers have increased receptive fields for extracting spatial features and one regression layer for outputting reconstructed values. By introducing the residual connection and the batch normalization algorithm, the structure is deepened and the training speed is accelerated. The experiments are carried out on six Chinese Gaofen-3 quad-polarimetric SAR datasets, and the results show that the proposed method has higher accuracy than the existing reconstruction algorithms with good generalization ability.

C-Band Right-Circular Polarization Ocean Wind Retrieval

We report an investigation of ocean-surface wind speed retrieval from C-band RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) images using a new channel of coright-circular polarization (RR-pol) in compact polarimetry (CP) option. The analysis of simulated RCM quad-polarized CP SAR data and collocated in situ buoy measurements suggests that the RR-pol is much less sensitive to wind directions than the other three polarizations in the CP option. A method is proposed for the RR-pol radar signal as a function of wind speed and incidence angle. We demonstrate that C-band RR-pol has the potential for ocean high wind retrieval, especially for cyclone studies.

Pre-Launch Assessment of RADARSAT Constellation Mission Medium Resolution Modes for Sea Oil Slicks and Lookalike Discrimination

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.

Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

Retrieval of Oil–Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar

The oil–water mixture ratio for oil spills on the ocean surface is an important parameter for volume estimation of oil spills, response strategy for the oil spills, cleanup operations, and remediation planning for the impacts on wildlife. Hybrid-polarized (HP) mode compact polarization (CP) synthetic aperture radar (SAR) imagery will soon be available with the launch of the RADARSAT Constellation Mission. The advantage of the proposed new SAR system is that CP images will have wider swath and shorter revisit time compared to quad-polarization (QP) images, which are presently available from space-borne and air-borne SAR. We present a methodology to retrieve the oil–water mixture ratio at the ocean surface using CP SAR imagery. We emulated the HP mode of CP SAR image using Uninhabited Aerial Vehicle SAR (UAVSAR) L band observations collected on June 23rd 2010 over the site of the Deep Water Horizon drilling rig. The gap between elements ratio of CP SAR covariance matrix and that of QP SAR Sinclair matrix is bridged. Numerical optimization and look up table methods are used to relate the oil–water mixture ratio to elements of the covariance matrix for the HP data backscatter. The mixture ratio estimates determined from the ratio of diagonal elements of the covariance matrix for HP mode CP data are compared with results retrieved from the co-polarization ratio from the original QP SAR observations. Results from the proposed methodology for SAR images captured in the HP mode of CP data are shown to compare favourably to observed in situ data of the mixture ratios.

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.

Assessment of C-band compact polarimetry SAR for sea ice classification

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 σHH0, σVV0, H and α, while for reconstructing the polarimetric parameters σHV0, ρH-V, λ1 and λ2, 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.

On the Use of Simulated Airborne Compact Polarimetric SAR for Characterizing Oil–Water Mixing of the Deepwater Horizon Oil Spill

Compact polarimetry (CP) synthetic aperture radar (SAR) is a form of coherent dual-pol SAR that has been shown to have great potential for maritime surveillance applications such as ship and ice detection. In this paper, we demonstrate the potential of CP data for oil spill characterization. As the availability of CP data is limited at this time, we simulate CP image data from UAVSAR L-Band quad-polarized images. We reconstruct quad-pol SAR data (termed pseudo-quad) from these simulated CP SAR data, and calculate an oil–water mixing index, termed Mdex. We show that the differences between the pseudo-quad and quad-pol Mdex maps are negligible. This contributes to the case that CP SAR has great potential for multiple applications in maritime surveillance.

Categorization of the Glaciated Terrain of Indian Himalaya Using CP and FP Mode SAR

This paper presents the comparison between the potential of synthetic aperture radar (SAR) data in compact polarimetry (CP) versus the fully polarimetric POLSAR mode (FP) for glacier areas. The eigenvalue-based constructed parameters, decompositions and supervised classification schemes were used for this inter-comparison. The comparison is focused on compact polarimetric techniques versus full polarimetric techniques based on both of qualitative and quantitative analysis of the glaciated parameters extraction. Overall performance of CP mode is lower than FP mode for glaciated terrain parameter extraction. Furthermore, it was found over the part of rugged Gangotri glaciated terrain, Indian Himalaya that the reconstruction pseudo polarimetry data is erroneous and highly biased because of data sets do not satisfy the pseudo polarimetric reconstruction conditions in the glaciated terrain.

Target Detection on the Ocean With the Relative Phase of Compact Polarimetry SAR

This paper discusses the potential for automatic ocean surveillance using compact linear polarization (CL-pol) synthetic aperture radar (SAR), with large area coverage. Here, the target is a wind farm in the North Sea. The relative phase, as derived from CL-pol SAR, is employed for detection of the wind turbines, apart from the wind turbines' wakes, based on fine-mode quad-polarization (quad-pol) RADARSAT-2 (RS-2) images. The relative phase of CL-pol measurements improves the contrast between the wind turbines and their wakes, because it has opposite signs for these two entities. Moreover, there is almost no variation in the relative phase with respect to wind speed or incidence angle. The results are verified by high-sea-state cases, up to 8.7-m significant wave height and 24.3-m/s wind speed, and also 641 quad-pol RS-2 SAR images collocated with 52 National Data Buoy Center buoys at different incidence angles and sea states. Thus, the relative phase of CL-pol SAR provides new light into the problem of operational autodetection of man-made targets, under high-sea-state conditions, over large areas.