RADARSAT-2 Images Research Articles

Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images

Soil moisture has great significance in the researches of hydrology, meteorology and agriculture yield estimation. The quad-polarimetric SAR images can provide a lot of polarimetric features, the significance of the features in surface parameter retrieval have attracted attentions in previous researches with no final conclusions because of the complexity of terrain scattering. In this paper, random forest regression (RFR) is used for both soil moisture retrieval and the importance evaluation of polarimetric features of Radarsat-2 images in winter wheat fields. According to the score of importance, feature selection and combination are done for modelling. We evaluate the retrieval accuracy of models with different feature combinations. The results show that models of important features selected by RFR have RMSE(root mean square error) less than 6% which are better results compared to traditional models; when compared with support vector regression and artifical neural networks, the RFR also shows best retrieval accuracies, which proves that RFR is suitable for soil moisture retrieval and feature selection. The high retrieval accuracies of LBC-CPD(linear backscatter coefficients-Cloude-Pottier decomposition) and LBC-CPR(linear backscatter coefficients-circular polarimetric ratio) indicates these features can improve the retrieval accuracy of soil moisture.

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

Potential of RADARSAT-2 to Improve Ice Thickness Calculations in Remote, Poorly Accessible Areas: A Case Study on the Slave River, Canada

River ice plays an important role in high latitude regions, and ice thickness is an important consideration to local residents as well as hydrologists. Two approaches, measurement (both remotely sensed and direct) and numerical calculations, are common methods used for ice thickness quantification. Measurement of ice thickness using SAR remote sensing approaches is still very challenging due to the low signals in thermal ice and limited penetration capability of C-band on consolidated ice covers. The numerical calculation can provide reasonable ice thickness estimation at a specific location but not the spatial variation. To carry out ice thickness estimations, numerous equations were proposed for numerical calculation. The Ashton equation, incorporating properties of ice and snow cover, was selected to determine the ice thicknesses along the Slave River in Canada. A framework (IceThick-RS) is proposed in this study, which incorporates polarimetric parameters of RADARSAT-2 images (quad-pol mode and C-band) into the ice thickness model. This framework was applied to estimate ice thicknesses along the Slave River. Direct measurements, gathered during the winter of 2014–2015, were used to calibrate the model. The results show that the IceThick-RS framework can be used to carry out river ice thickness estimation, especially in poorly accessible regions.

Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data

Wuhan city is the biggest city in central China and has suffered subsidence problems in recent years because of its rapid urban construction. However, longtime and wide range monitoring of land subsidence is lacking. The causes of subsidence also require further study, such as natural conditions and human activities. We use small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) method and high-resolution RADARSAT-2 images acquired between 2015 and 2018 to derive subsidence. The SBAS-InSAR results are validated by 56 leveling benchmarks where two readings of elevation were recorded. Two natural factors (carbonate rock and soft soils) and three human factors (groundwater exploitation, subway excavation and urban construction) are investigated for their relationships with land subsidence. Results show that four major areas of subsidence are detected and the subsidence rate varies from −51.56 to 27.80 millimeters per year (mm/yr) with an average of −0.03 mm/yr. More than 83.81% of persistent scattered (PS) points obtain a standard deviation of less than −6 mm/yr, and the difference between SBAS-InSAR method and leveling data is less than 5 mm/yr. Thus, we conclude that SBAS-InSAR method with Radarsat-2 data is reliable for longtime monitoring of land subsidence covering a large area in Wuhan city. In addition, land subsidence is caused by a combination of natural conditions and human activities. Natural conditions provide a basis for subsidence and make subsidence possible. Human activities are driving factors and make subsidence happen. Moreover, subsidence information could be used in disaster prevention, urban planning, and hydrological modeling.

Research on the Geo-Metrical and Radio-Metrical Topography Correction of the RADARSAT-2 Image

Terrain geometry and radiation characteristics of radar images have strong influence. The correction of these effects becomes indispensable when quantitative image analysis is performed with respect to the derivation of geo- and biophysical parameters. Based on RD geo-location model and digital elevation model, an ortho-rectification and TRC implementation flow was introduced. The correctness and effectiveness of the method were qualitatively and quantitatively evaluated through several experimentations. By comparing two kinds of effective TRC methods, we found the project-angle based method is more effective than the local incidence-angle based method. The rationality of computing effective TRC factor using initial SAR geo-location model was also demonstrated.

Mapping Spring Canola and Spring Wheat using Radarsat-2 and Landsat-8 Images with Google Earth Engine

Using remote sensing, it is difficult to accurately extract spring canola and wheat planting area with only optical images because both crops have the same growth period and similar spectral characteristics. Besides, optical images are susceptible to cloud contamination. Synthetic aperture radar is sensitive to canopy structure and is hardly influenced by weather; however, it is difficult to distinguish spring wheat and grass due to the similarity of both canopy structures during the major growth cycle. In order to resolve this problem, the present study proposed a method to extract spring canola and wheat by combining Radarsat-2 and Landsat-8 images based on Google Earth Engine. First, spring canola, forest, water and spring wheat and grass (both were regarded as one object) were extracted from Radarsat-2 image. Second, the cropland was extracted from Landsat-8 image. Third, synthetic mapping was carried out to achieve spring canola and wheat extraction. The result demonstrates that spring canola and wheat were successfully extracted with an overall accuracy of 96.04%.

Investigation of EM Backscattering from Slick-Free and Slick-Covered Sea Surfaces Using the SSA-2 and SAR Images

This paper is devoted to investigating the electromagnetic (EM) backscattering from slick-free and slick-covered sea surfaces at various bands (L-band, C-band, X-band, and Ku-band) by using the second-order small slope approximation (SSA-2) and the measured synthetic aperture radar (SAR) data. It is known that the impact of slick on sea surface is mainly caused by two factors: the Marangoni damping effect and the reduction of friction velocity. In this work, the influences induced by these two factors on the sea curvature spectrum, the root mean square (RMS) height, the RMS slope, and the autocorrelation function of sea surfaces are studied in detail. Then, the slick-free and slick-covered sea surface profiles are simulated using the Elfouhaily spectrum and the Monte-Carlo model. The SSA-2 with the tapered incident wave is employed to simulate the normalized radar cross-sections (NRCSs) of sea surfaces. Furthermore, for slick-free sea surfaces, the NRCSs simulated with the SSA-2 at various bands are compared with those obtained by the first-order small slope approximation (SSA-1), the classic two-scale model (TSM), and the geophysical model functions (GMFs) at various bands, respectively. For slick-covered sea surfaces, the SSA-2-simulated NRCSs are compared with those obtained from C-band Radarsat-2 images and L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) images, respectively. The numerical simulations illustrate that the SSA-2 can be used to study the EM backscattering from slick-free and slick-covered sea surfaces, and it has more advantages than the SSA-1 and the TSM. The works presented in this paper are helpful for understanding the EM scattering from the sea surface covered with slick, in theory.

THE USE OF SYNTHETIC APERTURE RADAR (SAR) DATA IN THE ANALYSIS OF INUNDATED AREAS DURING THE SPRING FLOOD

The paper discusses the opportunities of remote sensing data application as one of the main sources of information for monitoring river floods. Effective operation of flood forecasting systems requires reliable real-time data on inundation areas for timely calibration and verification of the used hydrodynamic models. The opportunity to obtain data from optical sensors might be limited because of dense cloud cover. Synthetic aperture radar (SAR) techniques are increasingly used today due to ability to operate independently of the surface illumination and the state of cloud cover receiving high spatial resolution data in near real-time mode. An important feature of SAR from space today is the increase in the number of freely distributed space data, in particular — images from Sentinel satellites developed by the European Space Agency. For instance, for the territory of Russia Sentinel-1 performs SAR imaging with 2–3 days coverage frequency. Within the framework of the project carried out by the authors, the research area is the city of Velikiy Ustuyg (Russia) located at the confluence of rivers Suhona and Ug. To identify flooded areas the RADARSAT-2 and Sentinel-1 images classification based on thresholding was carried out in open-source software. The visualization of the results was performed on the basis of information analytical system “Prostor”. The results of SAR data processing were compared with contours obtained on the basis of the calculation of the NDWI index from optical data from the Sentinel-2 and Resurs-P satellites. According to the spatial resolution of the data and the selected processing technology, it is possible to achieve high accuracy of flood mapping in open areas with low urbanization. The result confirms that SAR data can be successfully applied for operational flood forecasting.

The effects of orbit type on synthetic aperture RADAR (SAR) backscatter

ABSTRACTThe difference between an image pair acquired in crossing orbits of Synthetic Aperture Radar (SAR) sensors is of great importance. In this study, this difference was investigated in terms of backscatter and scattering mechanisms for several classes, including the Ground Vegetation (Gv), Water (Wtr), and Trees. Two RADARSAT-2 image pairs were used, one in spring and the other in winter. Almost all conditions were similar for each image pair except for the type of orbit. In the spring image pair, the backscatter for almost all classes from the image in the ascending orbit was more than that from the image captured in the descending mode. This was attributed to the change in the amount of evapotranspiration and the water content for the Gv and Tree classes, and was the result of wind speed variation for the Wtr class. For the winter image pair however, no significant difference was observed between the images acquired in the crossing orbits. The reason was that all the classes were frozen and, therefore, no evapotranspiration and roughness change occurred in that time. The results of this study proved that the difference between the images captured from crossing SAR orbits should be taken into account.

Influence of Incidence Angle in the Correlation of C-band Polarimetric Parameters with Biophysical Variables of Rain-fed Crops

A multi-temporal field experiment was conducted within the Soil Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain in order to retrieve useful crop information. The objective of this research was to evaluate the potential of polarimetric observations for crop monitoring by exploiting a time series of 20 quad-pol RADARSAT-2 images at different incidence angles (i.e. 25°, 31°, and 36°) during an entire growing season of rain-fed crops, from February to July 2015. The time evolution of 6 crop biophysical variables was gathered from the field measurements, whereas 10 polarimetric parameters were derived from the images. Thus, a subsequent correlation analysis between both datasets was performed. The study demonstrates that the backscattering ratios (HH/VV and HV/VV), the normalized correlation between HH and VV (γHHVV), and the dominant alpha angle (α1), showed significant and relevant correlations with several biophysical variables such as biomass, height, or leaf area index (LAI) at incidence angles of 31° or 36°. The joint use of data acquired with different beams could be exploited effectively to increase the refresh rate of information about crop condition with respect to a single incidence acquisition scheme.

Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice

Synthetic aperture radar (SAR) incidence angle has a significant effect on the microwave backscatter from sea ice. This paper investigates the incidence angle dependence of C- and L-band HH-polarized microwave backscatter coefficient over Arctic first-year sea ice (FYI) and multiyear sea ice (MYI) in winter. Advanced Land Observation Satellite Phased Array type L-band SAR (L-band) and RADARSAT-2 (C-band) images are used to derive ice type-specific incidence angle dependencies calculated using linear regression models. For L-band, mean ice type-specific incidence angle dependencies for FYI and MYI are −0.21 and −0.30 dB/1°, respectively; and for C-band, they are −0.22 and −0.16 dB/1°, respectively. To validate our results, we calculated root-mean-square deviation (RMSD) by comparing the ice type-specific dependence from 2010 with individual dependencies from 2009 based on ice types and frequencies. The RMSD is found to be smaller than the standard deviation of ice type-specific dependencies for both frequencies. The RMSD values for the L-band incidence angle dependencies are 0.03 and 0.04 dB/1° for FYI and MYI, respectively. For C-band, the RMSD values for the FYI and MYI dependencies are 0.03 and 0.01 dB/1°, respectively. Subsequently, we demonstrate that after applying incidence angle normalization, the variability of C- and L-band SAR backscatter reduces and separability of ice types increase substantially.

Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia

Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.

Evaluation of the Multilook Size in Polarimetric Optimization of Differential SAR Interferograms

The interferometric coherence is a measure of the correlation between two SAR images and constitutes a commonly used estimator of the phase quality. Its estimation requires a spatial average within a 2-D window, usually named as multilook. The multilook processing allows reducing noise at the expenses of a resolution loss. In this letter, we analyze the influence of the multilook size while applying a polarimetric optimization of the coherence. The same optimization algorithm has been carried out with different multilook sizes and also with the nonlocal SAR filter filter, which has the advantage of preserving the original resolution of the interferogram. Our experiments have been carried out with a single pair of quad-polarimetric RADARSAT-2 images mapping the Mount Etna's volcanic eruption of May 2008. Results obtained with this particular data set show that the coherence is increased notably with respect to conventional channels when small multilook sizes are employed, especially over low-vegetated areas. Conversely, very decorrelated areas benefit from larger multilook sizes but do not exhibit an additional improvement with the polarimetric optimization.

A C-Band Geophysical Model Function for Determining Coastal Wind Speed Using Synthetic Aperture Radar

A new geophysical model function (GMF), called C_SARMOD2, has been developed to relate high-resolution C-band normalized radar cross section (NRCS), acquired in VV polarization over the ocean, to the 10-m height wind speed. A total of 3078 RADARSAT-2 and Sentinel-1A VV-polarized synthetic aperture radar (SAR) images, acquired under different wind speed conditions, were collocated with in situ buoy measurements. The paired dataset was used to derive transfer functions and coefficients of C_SARMOD2, and then to validate the wind speed retrievals. The comparison between SAR-retrieved wind speeds and buoy measurements show almost no bias and a root mean square error of 1.84 m/s. Two representative quad- and dual-polarization SAR images, acquired from coastal regions, are used as case studies to examine C_SARMOD2 performances. The case study and statistical validation results suggest that the proposed C_SARMOD2 has the potential to measure coastal wind speeds at subkilometer resolutions. Although derived from low resolution NRCS measurements, this study also confirms the great robustness of CMOD5.N and recent CMOD7 when applied to SAR data. In addition, it shows that with the new generation of SAR satellite-borne sensors, it is no longer mandatory to rely on scatterometers in order to build a GMF, which will be used for SAR applications. Such an approach is particularly important in view of the upcoming RADARSAT Constellation Mission with new polarization configurations. Moreover, it also opens new perspectives on the derivation of GMFs in HH-polarization. However, these results also suggest that for coastal areas, the increase of the resolution to define the GMF is less important than adding other geophysical parameters to improve wind retrieval performance. This advocates for the necessity of revisiting the methodologies for ocean surface wind speed measurements in coastal areas.

A Preliminary Evaluation of the GaoFen-3 SAR Radiation Characteristics in Land Surface and Compared With Radarsat-2 and Sentinel-1A

The first evaluation of GaoFen-3 SAR radiation characteristics and its potential for quantitative parameter estimation in land surface is presented in this letter. Based on two triangle corner reflectors, five impulse response property parameters are calculated to assess image quality of GaoFen-3 SAR, and results show that the peak sidelobe ratio is superior to system specification and the integrated sidelobe ratio cannot be assessed by misconduct in the field campaign mainly for strong background scattering (not low enough). Five distributed target parameters are extracted from three categories and compared with Radarsat-2 and Sentinel-1A quasi-synchronous images in two days. The results indicate good radiometric resolution (RR) of 3 dB and good equivalent number of looks closed to 1 for GaoFen-3 single-look complex product, which proves GaoFen-3 SAR image is at the same quality level with the other two C-band SAR images and its RR meets system design of 3.5 dB. Then, the backscatter coefficient of these three SAR images is compared at bare soil area after GaoFen-3’s incidence angle being normalized to 43° by Oh2004 empirical model, and the results reveal the problem of several decibels lower for GaoFen-3 in absolute radiometric calibration. Finally, the capability of surface parameter estimation is justified by statistical relation of GaoFen-3 co-polarization backscatter coefficient and bare soil moisture content, indicating its good potential for quantitative applications in land after solving the problem in calibration.

Radarsat-1 image processing for regional-scale geological mapping with mining vocation under dense vegetation and equatorial climate environment, Southwestern Cameroon

The potential of Radarsat-1 beam mode Synthetic Aperture Radar (SAR) data processing for geological investigation in an equatorial environment has been evaluated. This approach used textural analysis based on Grey Level Co-occurrence Matrix (GLCM) on our image, followed by Principal Component Analysis (PCA) performed on eight normalized co-occurrence indices created (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) and directional filters for lithological discriminations and lineament investigations. Red-Green-Blue (RGB) color-composite was applied to three of the indices, the mean, variance and homogeneity, highlighting the morphostructure of the study area and facilitate lithology discrimination. The PC1 band was multiplied by itself (as PC1 × PC1 image) to enhance the information contained in this neo-canal and to reduce noise during filtering. Directional filters were then applied to the PC1 × PC1 image at 0°, 45°, 90° and 135° directions and the structure lines were extracted manually in a GIS software. From the results obtained, color-composite produced image map containing lithological units easily identified formations such as continental and coastal deposits, sedimentary stack, micaschists, garnet micaschists, micaceous quartzites, charnockitic orthogneisses, and coincided with those already existing on published geological map from Maurizot et al. (1986) and non-published geological map after IRGM geological field campaign. A total of 572 lineaments features (fractures and major faults) were identified on the filtered images and mapped. Major structures (faults) were considered as those clearly identified in the four directions while minor structures (fractures) were those observed in at least two directions. They are oriented in one of the two main directions: NE-SW (N040–N060) and NNW-SSE (N345–N360). The lineament result showed those that already existed on the reference maps and the newly updated lineaments. Spatial relationships between mapped lineaments and areas of current and historical mining exploration were examined by overall lineament density. GPS points of gold indices existing in the area correlate with areas of high lineament density particularly around the Ngovayang massif within the Paleoproterozoic Nyong unit. This study stresses the usefulness of remote sensing data and methods in field campaign, improvement of published geological maps and mining prospecting in areas with an equatorial climate.

Monitoring river ice cover development using the Freeman–Durden decomposition of quad-pol Radarsat-2 images

The monitoring of river ice development is a crucial guidance indicator to establish safe crossings along river ice covers. This is the first study, based on our knowledge, to understand the interactions between river ice cover structures and radar signals and to further monitor ice development using C-band synthetic aperture radar images. The study was applied to the Slave River, Canada, using the Freeman–Durden decomposition of quad-pol C-band Radarsat-2 FQ14W images and ice core crystallography analysis. Results demonstrate that the combination of volume and surface scattering can be used to monitor ice cover development that cannot be interpreted from single polarization images, such as Radarsat-2 spotlight images used in this study. These results indicate that the decomposed quad-pol Radarsat-2 images can provide a more effective guide than the single-pol Radarsat-2 SLA images to select safe ice transportation routes. This decomposition approach can be extended to other snow and ice covered rivers.

Hydrological Regime Monitoring and Mapping of the Zhalong Wetland through Integrating Time Series Radarsat-2 and Landsat Imagery

Zhalong wetland is a globally important breeding habitat for many rare migratory bird species. Prompted by the high demand for temporal and spatial information about the wetland’s hydrological regimes and landscape patterns, eight time series Radarsat-2 images were utilized to detect the flooding characteristics of the Zhalong wetland. Subsequently, a random forest model was built to discriminate wetlands from other land cover types, combining with optical, radar, and hydrological regime data derived from multitemporal synthetic aperture radar (SAR) images. The results showed that hydrological regimes variables, including flooding extent and flooding frequency, derived from multitemporal SAR images, improve the land cover classification accuracy in the natural wetlands distribution area. The permutation importance scores derived from the random forest classifier indicate that normalized difference vegetation index (NDVI) calculated from optical imagery and the flooding frequency derived from multitemporal SAR imagery were found to be the most important variables for land cover mapping. Accuracy testing indicate that the addition of hydrological regime features effectively depressed the omission error rates (from 52.14% to 2.88%) of marsh and the commission error (from 77.34% to 51.27%) of meadow, thereby improving the overall classification accuracy (from 76.49% to 91.73%). The hydrological regimes and land cover monitoring in the typical wetlands are important for eco-hydrological modeling, biodiversity conservation, and regional ecology and water security.

SEA OIL SPILL DETECTION USING SELF-SIMILARITY PARAMETER OF POLARIMETRIC SAR DATA

Abstract. The ocean oil spills cause serious damage to the marine ecosystem. Polarimetric Synthetic Aperture Radar (SAR) is an important mean for oil spill detections on sea surface. The major challenge is how to distinguish oil slicks from look-alikes effectively. In this paper, a new parameter called self-similarity parameter, which is sensitive to the scattering mechanism of oil slicks, is introduced to identify oil slicks and reduce false alarm caused by look-alikes. Self-similarity parameter is small in oil slicks region and it is large in sea region or look-alikes region. So, this parameter can be used to detect oil slicks from look-alikes and water. In addition, evaluations and comparisons were conducted with one Radarsat-2 image and two SIR-C images. The experimental results demonstrate the effectiveness of the self-similarity parameter for oil spill detection.