RADARSAT-2 Data Research Articles

Changes in Antarctic Ice-Shelf Margins between 1997 and 2019 using Sentinel and Radarsat Data

Changes in Antarctic Ice-Shelf Margins between 1997 and 2019 using Sentinel and Radarsat Data

Semisupervised Deep Convolutional Neural Networks Using Pseudo Labels for PolSAR Image Classification

Deep-learning-based methods have obtained satisfying results in polarimetric synthetic aperture radar (PolSAR) image classification. However, these methods require large numbers of labeled samples, which are usually time-consuming and high-priced for PolSAR images. To address this issue, a semisupervised method based on a 3-D convolutional neural network (3-D-CNN) using pseudo labels (PL-3-D-CNN) is proposed. First, the coherency matrix of PolSAR data is converted into a 6-D real-valued vector by a unitary transformation. Then, the K-means algorithm is utilized for generating pseudo labels. After that, labeled samples and pseudo labeled samples are fed into the PL-3-D-CNN model to extract supervised and unsupervised features. Finally, the supervised and unsupervised features are combined to improve classification accuracy. The proposed method is tested on both AIRSAR and RADARSAT-2 data sets. The results show that the proposed method is an effective method for PolSAR image classification and shows good performance under a small number of labeled samples. The source code for the PL-3-D-CNN model is available at https://github.com/fangzheng-nuaa/PL-3D-CNN.

Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed approach is currently the only phase unwrapping approach to take this strategy with its quality assurance. The proposed approach was found to improve upon the solution quality of the RGPU approach, in some cases achieving a tenfold decrease in root-mean-square error for simulated data. The PBRGPU approach performed well when applied to RADARSAT-2 data collected over Polar Bear Provincial Park (Ontario, Canada). The PBRGPU solutions were consistently on par with or superior to those generated by SNAPHU in terms of accuracy. While the PBRGPU approach does lag behind SNAPHU in terms of the domain of the solution, with SNAPHU unwrapping a significantly larger portion of the interferogram in all test cases, this issue could readily be mitigated through post-processing of the unwrapped interferogram. The proposed approach provides a solid foundation for region-growing algorithms that adapt to local noise levels and integrate all available information rather than relying on preprocessing strategies.

PolSAR additive noise estimation based on shadow regions

ABSTRACT Additive noise results from the thermal noise of the radar receiver or antenna, and often has an adverse effect on the subsequent applications. To weaken the influence of additive noise, it is crucial to know the actual additive noise level. However, the system noise level is not always provided in some single- or full-polarimetric synthetic aperture radar (PolSAR) products. As one of the typical low backscattering objects (LBOs) in PolSAR images, shadow occurs as soon as the depression angle of the synthetic aperture radar (SAR) system reaches a more value than the slope away from the radar illumination, and thus provides a feasible way to measure additive noise. This paper proposes a framework to automatically extract shadow regions by a trained model using the bag of words (BoW) model with data from different scenes. The additive noise can then be estimated across a large area based on the extracted shadow. The experiments based on single- and full-polarimetric Radarsat-2 data suggest that the shadow-based framework is an effective way to estimate additive noise.

Assessment of Wind Direction Estimation Methods from SAR Images

Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area.

Similarity Matrix Entropy for Multitemporal Polarimetric SAR Change Detection

For cumulative change detection using time-series polarimetric synthetic aperture radar (SAR) (PolSAR) data, pairwise comparisons using bi-temporal data change detection are often performed over each successive pair of images in the entire time series. In this way, due to the impact of the quality of the difference image (DI) of bitemporal data and the classification accuracy of the DI into the change and nonchange classes, there is a great risk of many false alarms or omissions caused by gradually occurred changes. Therefore, this letter proposes a symmetric similarity matrix based on a likelihood ratio test (LRT), which groups the similarity between any two-time point data in time series. Then, Shannon entropy is applied to the similarity matrix to calculate the DI of the entire time-series data. Finally, an image segmentation method based on Markov random field (MRF) is used to classify the calculated DI into the change and nonchange classes. The experiments using the multitemporal fully polarimetric Radarsat-2 data acquired at three different time points prove the effectiveness of the proposed method compared with some existing methods.

Estimation of Deformation Intensity above a Flooded Potash Mine Near Berezniki (Perm Krai, Russia) with SAR Interferometry

In this study we used RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar data for measuring subsidence above a flooded potash mine, which is almost entirely located within the city of Berezniki (Perm Krai, Russia), population 150,000. This area has experienced very fast subsidence since October 2006 when the integrity of the Berezniki-1 mine was compromised, resulting in water intrusion, subsequent flooding and closure of the mine. Due to the ongoing dissolution of carnallite, subsidence in this region is expected to continue in the foreseeable future. In addition to rapid subsidence, at least five sinkholes have formed in the region, with the largest being 440 × 320 m. We observed ground subsidence during the period October 2011–April 2014 (RADARSAT-2) with a vertical rate up to 14 cm/year and horizontal rate up to 10 cm/year; during the period July 2016–June 2020 (Sentinel-1) with a vertical rate up to 17 cm/year. Our results were validated by precise leveling, with a coefficient of correlation of 0.75. Subsidence faster than 17 cm/year observed by precise leveling was not resolvable with Differential Interferometric Synthetic Aperture Radar (DInSAR). Our results show the complementary nature of ground-based and space-borne measurement techniques. The precise leveling captures subsidence along profile lines with high precision but lower temporal resolution, while DInSAR captures subsidence with high spatial and temporal resolutions but with lower precision. DInSAR is also significantly affected by decorrelation outside of urban areas. An important advantage of our methodology is the ability to measure the horizontal east component of the ground deformation when both, ascending and descending, data are available. This measurement directly characterizes the level of anthropogenic load on buildings and infrastructure. We recommend continuing monitoring subsidence using both measurement techniques, which can also be complemented by continuous Global Navigation Satellite System (GNSS).

On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands

In this work, we study ground deformation of ocean-reclaimed platforms as retrieved from interferometric synthetic aperture radar (InSAR) analyses. We investigate, in particular, the suitability and accuracy of some time-dependent models used to characterize and foresee the present and future evolution of ground deformation of the coastal lands. Previous investigations, carried out by the authors of this paper and other scholars, related to the zone of the ocean-reclaimed lands of Shanghai, have already shown that ocean-reclaimed lands are subject to subside (i.e., the ground is subject to settling down due to soil consolidation and compression), and the temporal evolution of that deformation follows a certain predictable model. Specifically, two time-gapped SAR datasets composed of the images collected by the ENVISAT ASAR (ENV) from 2007 to 2010 and the COSMO-SkyMed (CSK) sensors, available from 2013 to 2016, were used to generate long-term ground displacement time-series using a proper time-dependent geotechnical model. In this work, we use a third SAR data set consisting of Radarsat-2 (RST-2) acquisitions collected from 2012 to 2016 to further corroborate the validity of that model. As a result, we verified with the new RST-2 data, partially covering the gap between the ENV and CSK acquisitions, that the adopted model fits the data and that the model is suitable to perform future projections. Furthermore, we extended these analyses to the area of Pearl River Delta (PRD) and the city of Shenzhen, China. Our study aims to investigate the suitability of different time-dependent ground deformation models relying on the different geophysical conditions in the two areas of Shanghai and Shenzhen, China. To this aim, three sets of SAR data, collected by the ENV platform (from both ascending and descending orbits) and the Sentinel-1A (S1A) sensor (on ascending orbits), were used to obtain the ground displacement time-series of the Shenzhen city and its surrounding region. Multi-orbit InSAR data products were also combined to discriminate the up–down (subsidence) ground deformation time-series of the coherent points, which are then used to estimate the parameters of the models adopted to foresee the future evolution of the land-reclaimed ground consolidation procedure. The exploitation of the obtained geospatial data and products are helpful for the continuous monitoring of coastal environments and the evaluation of the socio-economical impacts of human activities and global climate change.

Retrieving snow wetness based on surface and volume scattering simulation

Wetness is one of the important physical parameters of snowpack. Its spatial and temporal changes play a key role in snowmelt runoff forecast, regional climate change, and agricultural irrigation. In this study, we proposed a new method to retrieve snow wetness from full-polarimetric synthetic aperture radar (SAR) data. First, the dominant scattering components, i.e. surface and volume scattering, in wet-snow conditions were obtained by polarimetric decomposition. The random rough surface scattering models and the dense media radiative transfer (DMRT) model were then used to establish the surface and volume scattering models, through which the surface and volume scattering lookup tables were created, respectively. Based on the lookup tables and the polarimetric decomposition results, the snow surface and volume wetness were retrieved, respectively. Finally, the effective snow wetness was derived from the weighted summation of surface and volume wetness. The advantage of this method mainly comes from the full consideration the snow surface roughness, the local incidence angle on complex mountain terrain, and the polarimetric information. In experiments, the GaoFen-3 data obtained on January 17, 2018 in the Kelan River Basin and the Radarsat-2 data obtained on March 19, 2014 in the Manasi River Basin were selected to verify the applicability of the proposed method at different conditions. From the analysis of experiment results, the correlation coefficient between the estimated and the ground measured snow wetness in the Kelan River Basin is 0.72. The mean absolute error (MAE) and the root mean square error (RMSE) are 3.35% and 3.89%, respectively. The correlation coefficient between the snow wetness estimated by Radarsat-2 and the measured values in the Manasi River Basin is 0.62. MAE and RMSE are 1.32% and 1.62%, respectively. These results proved that the proposed method can effectively retrieve snow wetness under different SAR data, different areas, and different snow periods.

TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.

Mapping plastic-mulched farmland by coupling optical and synthetic aperture radar remote sensing

ABSTRACT Plastic Mulching (PM) is criticized for causing environmental problems in spite of the benefits to increase crop yield. Accurate map of Plastic-Mulched Farmland (PMF) is demanded to monitor the usage of plastic mulches and to mitigate its environmental damages. However, mapping PMF with remote sensing data over large scale is still technically challenging due to its variable spectral characteristic. This paper proposed a Random Forest (RF) workflow to map PMF by coupling multi-resource satellite observations, including high- and medium- resolution optical imageries and Synthetic Aperture Radar (SAR) imageries. Under the context of two typical PMF regions, Jizhou and Guyuan, China, we test the performance of the proposed method. Results indicated that coupling optical and SAR remote sensing data improved the mapping accuracy significantly. There were different strategies for optimal data coupling. Coupling of three data, which are the GaoFen-1 (GF-1) data, Landsat-8 data and Radarsat-2 data, generated the highest mapping accuracy. Besides, coupling the GF-1 data with SAR data produced better accuracy than coupling the Landsat-8 data with SAR data. Compared with the accuracies derived from the coupling of two data (such as coupling GF-1 data with Radarsat-2 data and coupling Landsat-8 data with Radarsat-2 data), coupling the three data improved the overall accuracies by 0.39 to 4.93%. Compared with the mapping accuracy produced from Radarsat-2 data alone, the overall accuracies generated from the optimum coupling increased by 20.96 and 26.06%, respectively, in Jizhou and Guyuan. When compared with the mapping accuracy of GF-1 data alone, the overall accuracies generated from the optimum coupling improved by 4.77% both in Jizhou and Guyuan. In general, contribution of optical remote sensing data was greater than that of SAR data for mapping PMF. Whereas, the SAR data also made great contribution to improve the mapping accuracy of PMF.

COLLAPSED BUILDING CLASSIFICATION WITH OPTICAL AND SAR DATA BASED ON MANIFOLD LEARNING

Abstract. The collapse of buildings is a major factor in the casualties and economic losses of earthquake disasters, and the degree of building collapse is an important indicator for disaster assessment. In order to improve the classification of collapsed building coverings (CBC), a new fusion technique was proposed to integrate optical data and SAR data at the pixel level based on manifold learning.Three typical manifold learning models, namely, Isometric Mapping(ISOMAP), Local Linear Embedding (LLE) and principle component analysis (PCA), were used, and their results were compared. Feature extraction were employed from SPOT-5 data with RADARSAT-2 data. Experimental results showed that 1) the most useful features of the optical and SAR data were contained in manifolds with low-intrinsic dimensionality, while various CBC classes were distributed differently throughout the low- dimensionality spaces of manifolds derived from different manifold learning models; 2) in some cases, the performance of Isomap is similar to PCA, but PCA generally performed the best in this study, yielding the best accuracy of all CBC classes and requiring the least amount of time to extract features and establish learning; and 3) the LLE-derived manifolds yielded the lowest accuracy, mainly by confusing soil with collapsed building and rock. These results show that the manifold learning can improve the effectiveness of CBC classification by fusing the optical and SAR data features at the pixel level, which can be applied in practice to support the accurate analysis of earthquake damage.

Investigating the Potential Use of RADARSAT-2 and UAS imagery for Monitoring the Restoration of Peatlands

The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time.

An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine

Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of backscatter intensities, which may influence the separability of the crop classes. This principle leads to multi-date classification approach. In this work, kernel principal component (KPCA) is adopted for feature selection from multi-date datasets, and the selected features are used for classification using support vector machine (SVM) classifier. The classification is investigated for both the KPCA-based SVM and only SVM approaches using quad-pol C-band RADARSAT-2 data acquired over the test site in Vijayawada, India. KPCA-based SVM classification shows an overall accuracy of 89%, which is better than 82% obtained using the SVM-based classification. The proposed methodology effectively incorporates the temporal crop information during classification.

Texture Classification-Based NLM PolSAR Filter

In this letter, a texture classification-based nonlocal means polarimetric SAR (NLM PolSAR) filter is introduced and named as texture classification-based filter (TBF). In this process, a classification algorithm that identifies the data into textural variations and heterogeneity due to speckle noise is presented. Also, a similarity metric is derived to estimate the patch similarity of K-distributed covariance matrices. Hence, a filter is proposed that processes the classified data suitably using NLM patch-based filters with either K- or Wishart distribution patch similarity metrics. The filtering performance of TBF is being analyzed on full-pol single-look RADARSAT-2 and four-look Airborne Synthetic Aperture Radar (AIRSAR) data.

Land subsidence and its relation with groundwater aquifers in Beijing Plain of China

Beijing is a major metropolis with significant land subsidence because of long-term overexploitation of groundwater. While the South-to-North Water Diversion Project (SWDP) has provided new water sources Beijing; it has changed the pattern of land subsidence evolution in Beijing since 2015. Here we address how land subsidence evolution before and after SWDP, and we quantify also the impact of groundwater level changes in different aquifers on land subsidence at spatial scale. Subsidence evolution before and after SWDP were compared by adopting Persistent Scatterer Inteferomotry (PSI) with Radarsat-2 and Sentinel-1 data. Spatial correlation between Interferometric Synthetic Aperture Radar (InSAR) derived subsidence and groundwater levels in four aquifers was investigated using the Random Forest (RF) machine learning algorithm and Geographical Detectors (GD) technique. Extensometer deformation data and corresponding variation in groundwater level observations at three monitoring stations were used for validations. The study reveals that: firstly, both InSAR-derived subsidence area and maximum annual deformation rate decreased from 79.2% and 141 mm/yr before SWDP, to 60.1% and 135 mm/yr after SWDP. A reduction of time series deformation at four subsidence centers started about two years after the commence of SWDP in 2015. Secondly, the variation of groundwater level in the second confined aquifer has the strongest spatial correlation with subsidence in all the aquifers, but its impact on this aquifer has decreased after SWDP. These findings have an important scientific significance for the rational allocation of water resources and management strategy for mitigating hazards associated with subsidence against the background of SWDP.

Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data

Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons.This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models.Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general.

Updating the subsidence map of Emilia-Romagna region (Italy) by integration of SAR interferometry and GNSS time series: the 2011–2016 period

Abstract. The analysis of the vertical movements of the soil in the Po River plane of the Emilia-Romagna Region (Italy) was updated through an interferometric analysis referred to the 2011–2016 time-span. This activity is a continuation of previous studies on the state of knowledge of vertical soil movements in the same area, analyzed firstly by levelling and GNSS and more recently by SAR interferometry for the periods 1992–2000, 2002–2006, 2006–2011, on behalf of the Emilia-Romagna Region. The survey area analysed was approximately 13 300 km2, which corresponds to the territory of the regional plain. The interferometric dataset was calibrated through the use of velocity time series of several permanent GNSS stations. Among the 36 stations analysed, 22 were included in the study area: 16 were used for the calibration and 6 as check points). The velocities required for the calibration of the SAR analysis were calculated in the period following the important seismic events that struck the territory of the Emilia Romagna Region in May 2012. The interferometric analysis was carried out by TRE ALTAMIRA using the SqueeSAR™ technology. In particular, in order to update the interferometric dataset to 2016, it was necessary to perform a joint processing of the available RADARSAT-1 data and of the data acquired by the RADARSAT-2 satellite using a specific operating mode of the SqueeSAR™ algorithm known as stitching; this approach allowed the joint processing of images acquired in the same geometry by these two satellites. The study of the time series of the GNSS permanent stations used to provide the velocity datum to the interferometric analysis, is described, and the results of the SqueeSAR™ interferometric processing are reported. Statistical analyses on the spatial distribution and the type of scatterers have been performed during the screening and validation procedures of the dataset, and for the identification and removal of the outliers. Finally, the resulting map is described in order to analyse the measured soil movements with respect to the results obtained in past analyses, and the possible geological and human-induced causes, which could have produced them.

Polarimetric SAR Speckle Filtering Based on Similarity Test and Adaptive Clustering

Speckle filtering of polarimetric synthetic aperture radar (PolSAR) data is a necessary preprocessing step for many subsequent applications. The performances of speckle reduction and details preservation are primarily determined by the scheme of similar pixel selection. This letter mainly contributes to establish a novel approach for adaptive and efficient selection of similar pixels. The core idea is to introduce the clustering concept to collect similar samples during the similarity test of polarimetric matrices which is functioned to be a distance measure. Adaptive clustering based on fast finding of density peaks of the data elements is adapted and an automatic determination strategy of cluster numbers is developed. Then the proposed speckle filter is established based on the distance measure and the adaptive clustering. Both unmanned aerial vehicle SAR (UAVSAR) and Radarsat-2 data sets are used for experimental studies. The demonstrations of similar pixel selection for several typical scattering patterns are carried out. The similar samples have been adaptively and precisely identified by the proposed scheme. Furthermore, the comparisons of speckle filtering performances with several advanced speckle filters clearly demonstrate the efficiency and superiority of the proposed method.

Mineral Oil Slicks Identification Using Dual Co-polarized Radarsat-2 and TerraSAR-X SAR Imagery

This study is devoted to a generalization of C-band Radarsat-2 and X-band TerraSAR-X synthetic aperture radar (SAR) data in the form of a diagram serving to easily identify mineral oil slicks (crude oil and emulsions) and separate them from the other oil slicks. The diagram is based on the multi-polarization parameter called Resonant to Non-resonant signal Damping (RND) introduced by Ivonin et al. in 2016, which is related to the ratio between damping within the slick of the short waves and wave breakings. SAR images acquired in the North Sea during oil-on-water exercises in 2011–2012 containing three types of oil spills (crude oil, emulsion, and plant oil) were used. The analysis was performed under moderate sea conditions (wind speeds of 2–6 m/s and sea wave heights of less than 2 m), the incidence angles of 27°–49°, and the signal-to-noise ratio (SNR) of −3 to 11 dB within slicks. On the diagram plane, created by the RND parameter and the Bragg wave number, the mineral oil samples form a well-outlined zone, called a mineral oil zone. For C-band data, the plant oil samples were clearly distinguished from the mineral oils in the diagram. Determination of the confidence level for the detection of mineral oils versus plant oil was proposed using the mineral oil zone boundaries. The mineral oil data with SNR within slicks better than 2 dB lay within this zone with a confidence level better than 65%. The plant oil data with the same SNR lay outside this zone with a confidence level of better than 80%. For mineral oil with SNR of −3 dB, the confidence level is 55%.