Compact Polarimetric Research Articles

Metasurface-based broadband full Stokes polarimeter with optimized simulation design

A polarimeter, fundamental for characterizing the polarization state of light, is critical for advancing optical measurement techniques by delivering precise polarization information. A compact and portable polarimeter holds particular importance in applications like remote sensing and medical diagnosis. However, existing methods for developing a compact polarimeter are difficult to achieve full Stokes vector detection for broadband operation, and the noise immunity is also very weak. These defects significantly constrain the versatility of polarimeters across diverse application scenarios. Herein, a metadevice with dual-layer subwavelength grating structure for full Stokes vector detection has been proposed, capable of simultaneously achieving broadband detection and noise suppression. The intensity of the four elliptical polarization states of the incident light can be captured by four regions on this metadevice, enabling the computation of the full polarization state information via Mueller matrix inversion. Additionally, a set of optimized retardance at 0.73π and orientation angles at 43°, 80°, 111°, and 146° is provided to effectively suppress the noise. The results indicate that the recovery error remains below 5% across the 450–650 nm spectral range, showcasing a 1.5-fold enhancement in noise suppression capability compared to conventional structures.

Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data

Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved.

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

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.

Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

Fine classification and phenological analysis of rice paddy based on multi-temporal general compact polarimetric SAR data.

Fine classification and phenological information of rice paddy are of great significance for precision agricultural management. General compact polarimetric (CP) synthetic aperture radar (SAR) offers the advantage of providing rich polarimetric information, making it an important means of monitoring rice growth. Therefore, in response to the current challenges of difficulty in rice type classification and the small differences in phenological polarimetric characteristics, a novel strategy for fine classification and phenological analysis of rice paddy is proposed. This strategy thoroughly explores the polarimetric information of general CP SAR data and the target scattering characterization capabilities under different imaging modes. Firstly, the general CP SAR data is formalized using the standard CP descriptors, followed by the extraction of general CP features through the ΔαB /αB target decomposition method. optimal CP features are generated to achieve fine classification of rice paddy. Finally, 6 phenological stages of rice are analyzed based on the general CP features. The experiment results of rice classification show that the classification accuracy based on this strategy exceeds 90%, with a Kappa coefficient above 0.88. The highest classification accuracies were observed for transplanting hybrid rice paddy (T-H) and direct-sown japonica rice paddy (D-J), at 80.9% and 89.9%, respectively. The phenological evolution rule of the two rice types indicate that from early June (seedling stage) to late July (elongation stage), the CP feature variation trends of T-H and D-J are generally consistent. However, from October (mature stage) to November (harvest stage), the variation trends of the CP features for T-H and D-J are significantly different. The study found that from the booting-heading stage to the harvest stage, the linear π/4 mode outperforms circular and elliptical polarimetric modes in distinguishing different types of rice. Throughout the entire phenological period of rice growth, CP SAR of linear π/4 mode is surpasses that of other linear modes in discriminating different type of rice. The proposed strategy enables high-precision fine classification rice paddy, and the extracted general CP αB parameter effectively reflects the phenological change trends in rice growth.

Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

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.

A simple polarimetric measurement based on a computational algorithm.

A simple and compact polarimeter comprising two electrically controlled liquid-crystal variable retarders (LCVRs) and a linear polarizer is demonstrated, which is enabled by analyzing the intensity variation of the modulated output light based on a computational algorithm. A proof-of-concept prototype is presented, which is mounted onto a power meter or a CMOS camera for the intensity data collection. The polarimetric measurement for the spatial variant polarization states of light is also verified, indicating the possibility of achieving a resolution-lossless polarimeter. Thus, our proposed method shows a cost-effective way to realize a compact polarimeter in polarization optics.

Single-shot polarimetry of vector beams by supervised learning

States of light encoding multiple polarizations - vector beams - offer unique capabilities in metrology and communication. However, their practical application is limited by the lack of methods for measuring many polarizations in a scalable and compact way. Here we demonstrate polarimetry of vector beams in a single shot without any polarization optics. We map the beam polarization content into a spatial intensity distribution through light scattering and exploit supervised learning for single-shot measurements of multiple polarizations. We characterize structured light encoding up to nine polarizations with accuracy beyond 95% on each Stokes parameter. The method also allows us to classify beams with an unknown number of polarization modes, a functionality missing in conventional techniques. Our findings enable a fast and compact polarimeter for polarization-structured light, a general tool that may radically impact optical devices for sensing, imaging, and computing.

A distributed land cover classification of FP and CP SAR observation using MapReduce-based multi-layer perceptron algorithm over the Mumbai mangrove region of India

ABSTRACT Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis.

A Hybrid Compact Polarimetry GNSS-R Analysis of the Earth’s Cryosphere

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

Calibration Strategy for Compact Polarimetric GNSS-R Instruments

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

Retrieval performances of different crop growth descriptors from full- and compact-polarimetric SAR decompositions

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 g3 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.

A Refined Model for Quad-Polarimetric Reconstruction from Compact Polarimetric Data

As a special dual-polarization technique, compact polarimetric (CP) synthetic aperture radar (SAR) has already been widely studied and installed on some spaceborne systems due to its superiority to quad-polarization; moreover, quad-pol information can be explored and reconstructed from the CP SAR data. In this paper, a refined model is proposed to estimate the quad-pol information for the CP mode. This model involves CP decomposition, wherein the polarization degree is introduced as the volume scattering model parameter. Moreover, a power-weighted model for the co-polarized coherence coefficient is proposed to avoid the iterative approach in pseudo-quad-pol information reconstruction. Experiments were implemented on the simulated Gaofen-3 and ALOS-2 data collected over San Francisco. Compared with typical reconstruction models, the proposed refined model shows its superiority in estimating the quad-pol information. Furthermore, terrain classification experiments using a complex-value convolutional neural network (CV-CNN) were performed on AIRSAR Flevoland data to validate the reconstruction effectiveness for classification applications.

Polarimetry for 3He Ion Beams from Laser–Plasma Interactions

We present a compact polarimeter for 3He ions with special emphasis on the analysis of short-pulsed beams accelerated during laser–plasma interactions. We discuss the specific boundary conditions for the polarimeter, such as the properties of laser-driven ion beams, the selection of the polarization-sensitive reaction in the polarimeter, the representation of the analyzing-power contour map, the choice of the detector material used for particle identification, as well as the production procedure of the required deuterated foil-targets. The assembled polarimeter has been tested using a tandem accelerator delivering unpolarized 3He ion beams, demonstrating good performance in the few-MeV range. The statistical accuracy and the deduced figure-of-merit of the polarimetry are discussed, including the count-rate requirement and the lower limit of accuracy for beam-polarization measurements at a laser-based ion source.

Oil Spill Detection by CP SAR Based on the Power Entropy Decomposition

In recent years, marine oil spills have adversely affected the marine economy and ecosystem, and the detection of marine oil slicks has attracted great attention. Combining different polarimetric features for better oil spill detection is a topic that needs to be studied in depth. Previous studies have shown that the compact polarimetric (CP) synthetic aperture radar (SAR) can be effectively applied to the detection of sea surface oil spill due to its own ability, which is conducive to the extraction of sea surface oil slick. In this paper, we apply the power–entropy (PE) decomposition theory, which decomposes the total scattered power according to the entropy contribution of each cell in the response, to CP SAR data for oil spill detection. The purpose of this study is to enhance the oil slick and the separability of the sea. As a result, an oil spill detection method based on the low-entropy radiation amplitude parameter lesa is proposed. We compare lesa with the other five popular polarimetric features and validate by quantitative evaluation that lesa is superior to other types of polarization feature parameters under different band data. Moreover, the random forest classification is performed on the feature map and achieves the visualization results of oil spill detection. The experimental results show that the lesa can combine the information of the two polarimetric characteristic parameters of entropy and total scattering power, and can clearly indicate the oil slick information under different scenarios.

Monitoring Crops Using Compact Polarimetry and the RADARSAT Constellation Mission

The RADARSAT Constellation Mission (RCM) can acquire imagery in Compact Polarimetric (CP) mode. With this new mode, and the increased revisit with three satellites, RCM can contribute to operational crop monitoring at national scales. The four Stokes (S0, S1, S2 and S3) and three m-chi decomposition (surface, double bounce, volume) parameters were used to identify crops (pasture/forage, barley, wheat, canola, flaxseed, peas, lentils) with a Random Forest classifier. The Stokes and m-chi parameters delivered maps of similar accuracies (95% overall accuracy) and were only slightly less accurate than a classification using optical satellite imagery (97%). To understand why Stokes parameters worked well in classifying crops, scattering responses for wheat, canola, lentils and peas were plotted on the Poincaré sphere. These responses were interpreted in the context of the degree of polarization and were related to crop phenology. These plots revealed that early and late in the season the polarized component of the scattered wave remained circular. However, in the active season when crop structure was changing, scattered waves became more elliptically polarized. Although the amount of polarized scattering was lower mid-season, the change in ellipticity was helpful in separating crop types.

Compact Polarimetric Synthetic Aperture Radar for Target Detection: A review

In recent years, compact polarimetric (CP) SAR has been widely used for Earth target detection as a means to balance system resources and target information. Although there has been a large number of related researches, an in-depth review of CP SAR, from basic principles to target detection methods, is lacking. This article aims to provide a review of this area. In this article, we review the historical development and application status of CP SAR, introduce the basic principles and the target detection principles of CP SAR, and summarize the currently proposed methods for CP SAR target detection. Over 200 publications are covered. We also discuss the challenges of current research and point out three promising research directions, i.e., the further deepening of feature extraction, deeper understanding of target characteristics, and development of intelligent detection techniques. We hope that this survey will help researchers to better understand this research area.

A compact full Stokes polarimeter

In this paper, we describe the design, development and characterization of a compact polarimeter. It works on the technique of rotating quarter wave plate to measure the full Stokes vector state of the incident light. The device integrates low cost commercial off-the-shelf components into a single block which eliminates the need of re-alignment and leads to a better precision and accuracy. The process of performing the intensity measurement and calculation of the polarization state has been fully automated using a microcontroller and a single-board computer i.e. Raspberry Pi. The total measurement time of the polarimeter is around 150 ms. The measurement accuracy and precision of the instrument are 0.5 degree and 0.025 degree respectively.

Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches, and that the GC algorithm provides the best optimization.