Compact Polarimetric Synthetic Aperture Radar for ... - IEEE Xplore

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 55, NO. 3, MARCH 2017

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Compact Polarimetric Synthetic Aperture Radar for Marine Oil Platform and Slick Detection Biao Zhang, Member, IEEE, Xiaofeng Li, Senior Member, IEEE, William Perrie, and Oscar Garcia-Pineda

Abstract— Compact polarimetric (CP) synthetic aperture radar (SAR) can provide ocean surface observations with large-coverage swath and abundant polarimetric scattering information. These distinctive characteristics make CP SAR a potential tool for operational monitoring oil slicks and oil platforms, overcoming the shortcomings of small spatial coverage by the traditional quad-polarization (quad-pol) SAR. In this paper, we use the RADARSAT-2 C-band quad-pol SAR data to generate CP covariance matrix elements and subsequently construct pseudoquad-pol backscatter coefficients, using two CP reconstruction algorithms to evaluate CP SAR’s applications in detection of oil slicks and oil platforms. The reconstructed co- and cross-polarization data show good agreement with original radar observations acquired at different incidence angles and wind speeds. Furthermore, we develop an unsupervised classification method using the relative phase, a logical scalar threshold that separates odd and multiple scattering events, as an indicator to discriminate oil slicks and platforms from clean ocean waters. The relative phase is positive for clean ocean surfaces where odd scattering is dominant, but negative for oil platforms and oil slickcovered areas associated with multiple scattering. The detections of oil spills and oil platforms are validated against known oil platform geographic positions and optical aircraft surveys of the oil slicks. The proposed method provides a promising technique to detect oil slicks and oil platforms from CP imaging mode SAR data, i.e., as may be acquired by the RISAT-1, ALOS-2, and the future RADARSAT Constellation Mission. Index Terms— Compact polarimetry, oil platform, oil slick, synthetic aperture radar (SAR). Manuscript received April 21, 2016; revised August 4, 2016; accepted October 26, 2016. Date of publication December 16, 2016; date of current version February 23, 2017. This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFC1401001, in part by the National Science Foundation of China for Outstanding Young Scientist under Grant 41622604, in part by the Excellent Youth Science Foundation of Jiangsu Province under Grant BK20160090, in part by the National Natural Science Youth Foundation of China under Grant 41206171, in part by the National Natural Science Foundation of China under Grant 41576032, in part by the Sino-Russian Cooperation Project under Grant 4141101049, in part by the National Oceanic and Atmospheric Ocean Remote Sensing Program, and in part by the Canada Office of Energy Research and Development. (Corresponding author: Biao Zhang.) B. Zhang is with the School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China, and also with the Jiangsu Research Center for Ocean Survey and Technology, Nanjing 210044, China (e-mail: [email protected]). X. Li is with GST, National Environmental Satellite, Data, and Information Service–National Oceanic and Atmospheric Administration, College Park, MD 20740 USA. W. Perrie is with the Ocean Ecosystem Science Division, Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada. O. Garcia-Pineda is with Water Mapping LLC, Tallahassee, FL 32306 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2016.2623809

I. I NTRODUCTION ONVENTIONAL single-polarization (single-pol) synthetic aperture radar (SAR) transmits and receives either horizontal or vertical polarized electromagnetic waves and obtains backscatter intensity signals from the illuminated area. In the single-pol implementation, the vector-backscattered wave is measured as a scalar quantity, and thus, additional information about the scattering process contained in the polarization properties of the scattered signals is lost. Fully polarimetric SAR, also known as the quad-polarization (quadpol) system, alternately transmits two orthogonal polarized pulses and receives the returns in two orthogonal polarizations measuring the scattering matrix of the imaging target, and therefore provides both amplitude and phase information for each image pixel. However, the drawback of quad-pol SAR is that it suffers an increase in pulse repetition frequency by a factor of two and an increase in data rate by a factor of four, in comparison to single-pol SAR. Moreover, although quad-pol SAR is capable of providing very high spatialresolution observations with complete polarimetric information, the smaller swath coverage makes it impractical for operational monitoring of marine oil slicks and oil platforms. Compact polarimetric (CP) SAR is a coherent dualpolarization (dual-pol) radar system. It requires that the relative phase between the two receive polarizations be retained [1], in distinct contrast to conventional dual-pol SAR, in which the relative phase is not available. CP SAR was originally proposed for the classification of lunar surface features and the earth’s surface vegetation and crops. There are two typical CP imaging modes: the “π/4” mode [2] and the “circular transmit, linear receive (CTLR)” mode [3]. The former mode transmits a 45° linear polarization waves and receives the returns in linear horizontal and vertical polarizations. In the latter “CTLR” mode, a circularly polarized wave is transmitted, and horizontal and vertical polarizations are received. The advantage of CP over quad-pol is that the CP increases the swath coverage and halves the transmitted power. In comparison with quad-pol, although CP cannot provide complete polarization backscattered information, the pseudoquad-pol covariance matrix can be reconstructed from simulated CP data [2], [4], yielding comparable polarimetric information content to that of the original quad-pol data [5]. Recent study reports that the classifications from CP SAR data achieve accuracy levels comparable to those from a quadrature-polarized SAR when using appropriate methodology, to within a few percents [6]. Different reconstruction

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algorithms based on the “π/4” and “CTLR” modes have been compared for land classification applications, using the airborne C- and L-band quad-pol SAR imagery of urban features, grass, and forests [4], [7]. These algorithms make the construction of the pseudoquad-pol data with various degrees of accuracy. It is found that the “CTLR” mode outperforms the “π/4” mode, with respect to the polarimetric decomposition parameters, such as entropy and mean scattering angle [4]. However, the “π/4” mode reconstruction algorithm has been tested and applied to ocean targets, demonstrating that the “π/4” mode with constant parameter (N = 4) can achieve good ship detection performance [8]. In particular, 33 RADARSAT-2 quad-pol SAR images were used to estimate variable N(where N is roughly the ratio of the double-bounce backscatter to the volume backscatter [4]) and found that the mean values of N decreases as the radar incidence angle increases [8]. Moreover, icebergs were detected using the reconstructed pseudoquad-pol data and the likelihood ratio method, which showed that the use of the reconstructed crosspol intensity can improve the detection performance of a CP SAR system [9]. The ship detections method was also examined with the original quad-pol data and pseudoquadpol data from the “CTLR” mode, and demonstrated that the reconstructed cross-pol data are able to achieve a performance comparable to that of quad-pol data [10]. Our study area is Gulf of Mexico, which is one of the most important regions for energy resources and infrastructure in the world. Gulf of Mexico federal offshore oil production accounts for 17% of total U.S. crude oil and federal offshore natural gas production (http://www.eia.gov/special/gulf_of_mexico/). There were 3363 oil platforms in the Gulf of Mexico recorded in the Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), formerly the Minerals Management Service, database as of July 2016, with no associated removal date (http://www.boem.gov/). These offshore oil platforms are often exposed to hurricanes, which pose a serious threat to the safety and stability of the platform and the overall structural systems. For example, two of the most destructive weather events, hurricanes Katrina and Rita in 2005, caused some oil platforms to break from their moorings. The damaged platforms not only lead to the loss of operation but also threaten the marine environment. Moreover, oil platforms may shift tens of kilometers from their original locations due to strong hurricane-induced winds. In general, under these conditions, oil companies spend about a day to locate a delocated platform and then approximately one week to tow it back to its original position. Spaceborne SAR is a very useful remote sensing instrument for monitoring ocean surface features, including ships and oil platforms as well as oil slicks because of its almost all-weather, day and night observational capability. Conventional singlepol RADARSAT-1 SAR data had been used to automatically detect ships with improved constant false alarm rate (CFAR) algorithm [11], [12]. Compared with single-pol SAR observations, quad-pol SAR observations are more sensitive to the properties of metallic objects, such as oil platforms and ships at sea, and are thus more suitable for observation and recognition of these targets [13], [14]. Moreover, the degree

of polarization in linear and compact dual-pol SAR data was used to detect ships and oil platforms [15]. Monitoring of offshore oil platforms for “situation preparedness” using quad-pol or CP SAR can deliver critical information during emergency scenarios in several ways: 1) by reducing the need for more expensive observation on the ground or by aircraft; 2) by detecting oil slicks from damaged platforms; and 3) by identifying changes in platform locations caused by hurricanes. Oil slicks (natural seeps and oil spills from ships or platforms) are abundant in the Gulf of Mexico and other marginal seas worldwide [16]–[20], and the locations of many active slicks have been verified by submersible sampling [21]. The oil slicks from natural seeps are perennial features, usually relatively small in volume, and confined to discrete geographic areas [22]. Optical sensors, for example, the Moderate Resolution Imaging Spectroradiometer (MODIS), can be used to detect natural oil slicks [23], but the detection is dependent on cloudless daytime conditions. The advantage of SAR over optical instruments is that the SAR can observe oil slicks even in cloudy and nighttime conditions. The application of airborne and satellite optical and microwave sensors for marine oil spill remote sensing has been reviewed and summarized in detail [24]–[26]. There are several methods that have been applied to detect oil slicks using single-pol SAR imagery, including Bayesian image classification method [27], statistical algorithms using fuzzy logic [28], and neural network [29]–[32]. However, compared with single-pol SAR observations, the multifrequency and multipolarization SAR offers an enhanced ability to observe oil slicks [33]–[36]. Owing to their complete polarimetric information content, the C- and L-band quad-pol SAR data have been widely used to map sea oil slicks [37]–[38] and to analyze and identify the Deepwater Horizon crude oil slicks in the Gulf of Mexico, which started on April 20, 2010 [39]–[42]. CP SAR systems have been explored for their ability to detect oil slicks, due to their wide swath imaging ability and abundant polarimetric information [43]–[46]. The recent study reports that the CP SAR architectures have potential to represent an interesting operational alternative for both detecting oil slicks and discriminating them from weak-damping surfactants [47]. Currently, the Indian mission RISAT-1, launched in 2012, is equipped with a C-band SAR that implements the “CTLR” mode. The L-band SAR operated by the Japanese ALOS-2 mission, launched in 2014, allows both the “π/4” mode and “CTLR” mode. The future RADARSAT Constellation Mission (RCM) will operate the “CTLR” mode, providing wide-coverage swath data with abundant polarimetric information and offering a great potential for operational monitoring of oil slicks and oil platforms. To better understand the potential marine applications of CP data, in this paper, we derive CP data using original quad-pol data acquired at different radar configurations and wind conditions, and then construct the pseudoquad-pol covariance matrix elements. Furthermore, we develop an unsupervised classification method to distinguish oil platforms and oil slicks from clean ocean waters using the reconstructed data. The important contributions of this paper consist of two focus elements.

ZHANG et al.: CP SAR FOR MARINE OIL PLATFORM AND SLICK DETECTION

CTLR Ccp =

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2 ∗ ∗ ∗ + S∗ · S 1 1 |SHV |2 −i |SHV |2 1 −2 SHH · SHV SHH · SHV VV HV |SHH | ∗ SHH · SVV + + ∗ ·S ∗ ∗ 2 SVV · SHV |SVV |2 2 −i SVV · SHH 2 i |SHV |2 |SHV |2 2 SHH HV + SVV · SHV (6)

1) We first evaluate the two different CP SAR reconstruction algorithms for ocean target object detection using a large data set acquired under various wind speeds and radar incidence angles. 2) We develop a physically based unsupervised classification method, based on different scattering mechanisms, to discriminate oil platforms and slicks from the clean sea surface. The remainder of this paper is organized as follows. Section II describes the quad-pol SAR data and in situ observations. Section III summarizes the CP reconstruction algorithms and describes the proposed oil slick and oil platform detection method. Section IV presents the results and discussions. A summary is provided in Section V. II. DATA S ETS A. SAR Data In this paper, we use the C-band RADARSAT-2 fine quadpol imaging mode SAR data. A nominal image scene covers an area of approximately 25 × 25 km. These scenes are collected from 30 beam modes (FQ1 ∼ FQ30) with a resolution of 8.0 m in the azimuth and 5.4 m in the range directions, covering incidence angles between 20° and 49°, from the near to far range. The pixel spacings in the azimuth and range directions are 4.73 and 4.74 m, respectively. The noise-equivalent sigmazero (NESZ) is a measure of the sensitivity of the radar to areas of low backscatter. NESZ is defined as the scattering crosssectional coefficient of an area that contributes a mean level in the image, equal to the additive noise level. RADARSAT-2 has a very low NESZ in comparison with other spaceborne SAR systems in orbit. The NESZ for fine quad-pol mode data is approximately −36.5 ± 3 dB [48]. We collected 1594 quad-pol SAR images from different geographic locations in the Gulf of Alaska, East and West coasts of the U.S., and the Gulf of Mexico between October 2008 and January 2013. There is one National Oceanic and Atmospheric Administration National Data Buoy Center (NDBC) buoy in each SAR image domain. In the data set, buoy measurements and the SAR observations were matched within 30 min. The radar incidence angle and quad-pol covariance matrix elements [|Shh |2 , |Svv |2 , |Shv |2 , Shh (Svv )∗ ] at each buoy position were estimated and matched up with buoy-measured wind speeds. In the collocated data set, the entire range of incidence angle and wind speeds is between 20.9° and 49.3° and 1.0 and 24.3 m/s. The co-pol backscatters have an excellent signal-to-noise ratio due to strong radar returns, particularly for smaller incidence angles. The crosspol backscatters have a relatively smaller signal-to-noise ratio at all incidence angles. This data set is used to construct the pseudoquad-pol data using the original quad-pol data, and also, to assess the reconstruction performance. Moreover, we also collected 29 quad-pol SAR images acquired over the Gulf of

Mexico, which are first converted into pseudoquad-pol data, and then used to detect oil platforms and slicks. B. Oil Platform and Aircraft Oil Slick Data The BOEMRE database provides the most recent, comprehensive inventory of process-level offshore oil platform locations (longitudes and latitudes) in the Gulf of Mexico, making it the best source available to validate the platform detection results. The combination of BOEMRE database and SAR detections also has the benefit that it provides monitoring of changes in oil platform locations. Moreover, aerial optical photographic surveys of oil slicks were carried out synchronously with the acquisition of the RADARSAT-2 SAR data. The photographs were collected on board a Cessna (property of onwingsofcare.org) flying at approximately 360 m over the oil slicks in the Gulf of Mexico. These aircraft optical observations are used to verify the CP SAR oil slicks detections, and exclude look-alike phenomena. III. M ETHODOLOGY A. CP Data Generation The scattering vector for a quad-pol SAR is given by kqp = [SHH SHV SVH SVV ]T.

(1)

Under the assumption of scattering reciprocity, the SHV and SVH scattering vector elements are equal (SHV = SVH ). The corresponding quad-pol SAR covariance matrix is generated from the outer product of the scattering vector with its conjugate transpose as ∗T Cqp = kqp · kqp ⎡ ⎤ √ ∗ ∗ SHH · SVV 2 SHH · SHV |SHH |2 √ ⎢√ ⎥ ∗ 2 ∗ ⎦ = ⎣ 2 SHV · SHH √ 2|SHV | ∗ 2 SHV ·2SVV ∗ SVV · SHH SVV | 2 SVV · SHV | (2) where · and ∗ indicate spatial average and complex conjugate, respectively, and T denotes the transpose. The scattering vector for the CP “π/4” and “CTLR” modes can be written in terms of the linear–transmit–linear–receive components of the quad-pol scattering vector √ π/4 = [SHH + SHV SVV + SHV ]T / 2 (3) kcp √ CTLR T = [SHH − i SHV − i SVV + SHV ] / 2. (4) kcp The CP covariance matrix for the “π/4” mode is ∗ 1 1 |SHV |2 |SHV |2 SHH · SVV |SHH |2 π/4 Ccp = + ∗ |SVV |2 2 SVV · SHH 2 |SHV |2 |SHV |2 ∗ ∗ + S∗ · S 1 SHH · SHV 2 SHH · SHV HV VV + (5) ∗ ·S ∗ ∗ 2 SVV · SHV 2 SHH HV + SVV · SHV and for the “CTLR” mode, shown at the top of the page.

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Fig. 1. C-band RADARSAT-2 fine quad-pol SAR image acquired at 12:01 UTC on July 14, 2011. (a) |SHH |2 . (b) |SVV |2 . (c) |SHV |2 . RADARSAT-2 Data and Product MacDonald, Dettwiler, and Associates Ltd., All Rights Reserved.

In the existing reconstruction algorithms [2], [4], the reflection symmetry assumption [49] is used to remove the products of co- and cross-pol backscattering coefficients in the covariance matrix. Reflection symmetry implies complete decorrelation between the co- and cross-pol backscattering coefficients, ∗ = S ∗ for example, SHH SHV HV SVV = 0. After the reflection symmetry relation is applied, the CP covariance matrix of the “π/4” and “CTLR” modes can be simplified as ∗ + |S |2 1 SHH · SVV |SHH |2 + |SHV |2 HV π/4 Ccp = (7) ∗ + |S |2 |SVV |2 + |SHV |2 2 SVV · SHH HV ∗ − |S |2 2 2 1 HV CTLR |SHH |2 + |SHV |∗ i SHH SVV = Ccp . |SVV |2 + |SHV |2 2 i |SHV | − SVV SHH (8) Since the RADARSAT-2 fine quad-pol single look complex products provide measurements of SHH , SHV , and SVV , we can estimate each element of the CP covariance matrix using the original quad-pol data.

B. Pseudoquad-Pol Data Reconstruction The reconstruction of the pseudoquad-pol covariance matrix from CP data can be summarized in two steps: 1) derivation of the CP data from quad-pol data and 2) reconstruction of the pseudoquad-pol covariance matrix with CP data. We describe the first step in detail in Section III-A. As the 2 ×2 CP covariance matrix contains only four independent parameters, an additional constraint is required to construct the pseudoquadpol data from the CP data. As a constraint, a relationship between the magnitude of the linear coherence and the crosspol ratio was proposed in [2], that is (1 − |ρ|) |SHV |2 = 2 2 |SHH | + |SVV | 4 where

∗ SHH · SVV

(9)

ρ= . |SHH |2 · |SVV |2

(10)

Fig. 2. Scatter plot of the two sides of (9). The diagonal line indicates where data would lie if (9) was a strict equality. Plotted on the abscissa and ordinate are |SHV |2 /(|SHH |2 + |SVV |2 ) and (1 − |ρ|)/4, respectively, which are derived from the original quad-pol data, as shown in Fig. 1.

In some cases, the left-hand side of (9) is not equal to the right-hand side, because the denominator on the righthand side should be a variable not a constant. The variable N is roughly the ratio of the double-bounce backscatter (|SHH − SVV |2 ) to the volume backscatter (|SHV |2 ) [4] |SHH − SVV |2 N= . (11) |SHV |2 The reconstruction for the “π/4” mode starts with an initial guess 12 Ccp ρ(0) = 11 · C 22 Ccp cp

(12)


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Fig. 3. Reconstructed pseudoquad-pol data (a) |SHH |2 , (b) |SVV |2 , and (c) |SHV |2 from original quad-pol data, as shown in Fig. 1, with the “π/4” mode algorithm (N = 4).

Fig. 4. Scatter plot of the original quad-pol data shown in Fig.1 versus the reconstructed pseudoquad-pol data shown in Fig. 3. (a) for HH, and (b) for VV, and (c) for HV.

|SHV |2(0)

11 22 Ccp + Ccp 1 − |ρ(0) | · = 2 3 − |ρ(0) |

(13)

and then proceeds to an iteration of the following equation: 12 − |S |2 Ccp HV (i) ρ(i+1) = 2 11 22 − |S |2 Ccp − |SHV |(i) · Ccp HV (i)

For the “CTLR” mode, the initial guess ρ(0) and the i + 1 iteration value of |SHV |2 are different from those in the “π/4” mode, and are given by 12 −i Ccp ρ(0) = 11 C 22 Ccp cp

(14)

ij

until it converges. The terms Ccp are the corresponding elements of the 2 × 2 CP covariance matrix. For the Souyris reconstruction algorithm [2], this approach leads to 11 + C 22 Ccp 1 − |ρ(i+1) | cp |SHV |2(i+1) = (15) 2 3 − |ρ(i+1) | whereas, for the Nord reconstruction algorithm [4], the result is 11 1 − |ρ(i+1) | 22 |SHV |2(i+1) = Ccp + Ccp . (16) N + 2(1 − |ρ(i+1) |) Given a value for |SHV |2 , for the “π/4” mode, the pseudoquadpol covariance matrix is then constructed by ⎡ 11 − |S |2 12 − |S |2 ⎤ 0 Ccp Ccp HV HV π/4 2 ⎦. (17) 0 2|S | 0 Cpq = ⎣ HV 12 − |S |2 ∗ 22 − |S |2 Ccp 0 C HV HV cp

ρ(i+1) =

(18)

12 + |S |2 −i Ccp HV (i) . 2 11 − |S |2 22 Ccp HV (i) Ccp − |SHV |(i)

(19)

The reconstructed pseudoquad-pol covariance matrix is CTLR Cpq ⎡

11 − |S |2 Ccp HV 0 = ⎣ 12 + |S |2 ∗ − i Ccp HV

0 2|SHV |2 0

12 + |S |2 ⎤ −i Ccp HV ⎦. 0 22 − |S |2 Ccp HV (20)

In summary, the CP reconstruction algorithms provide a convenient framework to generate the approximate pseudoquad-pol data.

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Fig. 5. C-band RADARSAT-2 fine quad-pol SAR image acquired at 01:56 UTC on June 17, 2010. (a) |SHH |2 . (b) |SVV |2 . (c) |SHV |2 . RADARSAT-2 Data and Product MacDonald, Dettwiler, and Associates Ltd., All Rights Reserved.

where √ √ 13 12 23 + (1/ 2) Cpq + Cpq (22) S3 = (1/ 2) Cpq √ √ 13 12 23 22 S4 = −(1/ 2) Cpq − (1/ 2) Cpq + Cpq −1/2Cpq (23) where and represent the real and imaginary parts, respec12 , C 13 , C 22 , and C 23 are the elements of the tively, and Cpq pq pq pq reconstructed pseudoquad-pol covariance matrix. Thus, we can rewrite the relative phase as HH · S VV ∗ − S HV 2 Spq pq pq δ = a tan . (24) HH · S VV ∗ Spq pq


C. Oil Slick and Oil Platform Detection Methodology CP SAR is a coherent dual-pol radar system, providing both the amplitude and the phase that allow characterization of the backscatter field. Polarimetric characteristic indicators, for example, the degree of polarization, circular polarization ratio, and relative phase, can be estimated from Stokes parameters [3]. Of these indicators, the relative phase is particularly useful, because it is a logical scalar, with positive or negative signs, indicating different scattering mechanisms. The Stokes parameters measured in the backscatter field can be easily evaluated using quad-pol covariance matrix elements. Here, we use the reconstructed pseudoquad-pol data to calculate the Stokes parameters and then, the relative phase. The relationships between the relative phase (δ) and the Stokes parameters (S3 and S4 ) are given by δ = a tan(S4 /S3 ) − 180° ≤ δ ≤ 180°

(21)

In general, there are three simple scattering mechanisms: surface scattering (single or odd-bounce), dihedral scattering (double or even-bounce), and volume scattering (diffuse or multiple-bounce). Odd and even-bounce scattering mean that a scattered wave from a plane surface or from a corner reflector surface suffers an odd or even number of reflections before reaching the radar receiver. Moreover, forest and heavy vegetation have the characteristics of multiplebounce scattering [50]. If there is an odd scattering event, HH and S VV are in phase. the scattering matrix elements Spq pq HH · (S VV )∗ will be near zero Thus, the phase angle of Spq pq HH · (S VV )∗ ]| > |S HV |2 and the resulting relationship | [Spq pq pq is to be obtained [51]. In this case, the relative phase is positive. Considering the case of multiple scattering event, the HH- and VV-polarized radar returns are almost completely uncorrelated. The multiple scattering is present such as in the case of scattering from forested areas [52], or scattering by extremely rough surfaces or surfaces covered by blocky boulders. For the multiple scattering, the expected HH · (S VV )∗ ]| will be nearly zero, and thus value of | [Spq pq

HH · (S VV )∗ ]| < |S HV |2 is to be obtained. In this sit| [Spq pq pq uation, the relative phase is negative. In general, the scattering of electromagnetic waves from slightly rough ocean surfaces can be assigned to the odd scattering class [51], whereas scattering from oil slick-covered areas and oil platforms is possibly associated with the multiple scattering class [13], [53]. Therefore, we can implement an unsupervised classification to


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Fig. 7. Reconstructed pseudoquad-pol data (a) |SHH |2 , (b) |SVV |2 , and (c) |SHV |2 from original quad-pol data shown in Fig. 5, with the “CTLR” mode algorithm (N = |SHH − SVV |2 /|SHV |2 ).

Fig. 8. Scatter plot of the original data shown in Fig. 5 versus the reconstructed pseudoquad-pol data shown in Fig. 7. (a) for HH, and (b) for VV, and (c) for HV.

Fig. 9. C-band RADARSAT-2 fine quad-pol SAR image acquired at 12:09 UTC on June 9, 2015. (a) |SHH |2 . (b) |SVV |2 . (c) |SHV |2 . RADARSAT-2 Data and Product MacDonald, Dettwiler, and Associates Ltd., All Rights Reserved.

distinguish oil slicks and oil platforms from the clean ocean waters, using the relative phase parameter. Although the quad-pol SAR is capable of achieving high spatial-resolution observations, including complete polarimetric information, it is less suitable for operational monitoring of oil slicks and oil platforms due to its small swath coverage. Although the conventional dual-pol SAR can provide large swath observations with medium resolution, it does not acquire complete information pertaining to the full polarization state of the target. Therefore, the classification accuracy of dualpol is not better than that of quad-pol [7]. CP SAR is able to supply polarimetric information content comparable to that of quad-pol and can also provide ocean surface observations with large area coverage. As mentioned in Section III-B, the

CP covariance matrix elements are first derived from original quad-pol data, and then pseudoquad-pol data are reconstructed using CP data. In this paper, we use reconstructed pseudoquadpol data to calculate the relative phase for the detection of natural oil slicks and oil platforms in the Gulf of Mexico. The future RCM, to be launched in 2018, will provide CP imaging mode SAR data. At that time, the pseudoquad-pol data will be directly estimated from wide swath CP observations. IV. R ESULTS AND D ISCUSSION A. Reconstruction Performance Based on different reconstruction algorithms, we use three typical C-band RADARSAT-2 fine quad-pol SAR images of oil platform, oil slick, and ocean surface wind features, to

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first derive CP data and then, to construct the pseudoquad-pol covariance matrix elements. Fig. 1 shows a quad-pol SAR image of the Gulf of Mexico acquired at 12:01 UTC on July 14, 2011. The radar incidence angles are between 26.9° and 28.7° from the near to far range. The mean wind speed is approximately 6.5 m/s as calculated using a cross-polarization wind speed retrieval model [54]. The bright spots shown in the co- and cross-pol SAR images are the offshore oil platforms in the Gulf of Mexico. We estimate the left- and right-hand sides of (9) and illustrate these quantities in Fig. 2. Note that the both sides of the equation are approximately equal. Thus, it is reasonable to set N to be a constant, 4. We use the “π/4” mode approach, with constant N, to construct the pseudoco-polarized and pseudocross-polarized backscatters, as shown in Fig. 3. For the purpose of assessment of the reconstruction performance, we perform a quantitative comparison between the original quad-pol data and the reconstructed pseudoquad-pol data. Fig. 4 shows that the errors in reconstructing the co-pol backscatter are quite small, but the reconstruction of cross-pol backscatter is not better than that of co-pol cases because of the much lower backscatter signal level. The bias and root mean square (rms) error of the reconstructed cross-pol backscatter are −0.381 and 0.977 dB, respectively. We also apply the “CTLR” mode approach with constant N to construct the pseudoquad-pol data, and compare the results with those derived from the “π/4” mode. The bias and rms error of reconstructed cross-pol backscatter are 0.826 and 0.947 dB, respectively. Additional comparisons of these cross-pol reconstruction results from “CTLR” mode are not shown. In this case, the “CTLR” mode has a larger bias than the “π/4” mode for cross-pol reconstruction. The “CTLR” mode overestimates the pseudocross-pol backscatter compared with the “π/4” mode, although both possess almost the same rms error.

Fig. 5 shows a quad-pol SAR image of the U.S. West Coast acquired at 01:56 UTC on June 17, 2010. The radar incidence angles in the near and far ranges are 31.3° and 33.0°. Ocean surface wind features are clearly visible in this image. There is an NDBC buoy (#46047) presented in the image domain. The buoy-measured wind speed is 10.7 m/s. To construct the pseudoquad-pol data, again, we estimate the left- and righthand sides of (9) and determine whether constant or variable N should be selected. As shown in Fig. 6, the values of the left-hand side are significantly larger than those of the righthand side. This indicates that the constraint [2] proposed by for constructing the pseudoquad-pol data from the CP data does not always hold. In this case, the value of N should be a variable, rather than a constant of 4, which can be estimated using the ratio between double-bounce backscatter and volume backscatter [4]. We combine the “CTLR” mode with the estimated N values to construct the pseudoquad-pol data. The reconstructed results are shown in Fig. 7. A scatter plot of the original quad-pol data against reconstructed pseudoquad-pol data is given in Fig. 8. The reconstructed and original co-pol backscatters are very close, with very small bias and rms error. For the cross-pol reconstruction, the bias and rms error are 0.204 and 0.391 dB, respectively. The NESZ minimum of the SAR image shown in Fig. 5 is −35.9 dB. The reconstructed cross-pol backscatters are all above this NESZ minimum, which are shown in Fig. 7(c). By comparison, when we use the “π/4” mode and the calculated N values to construct pseudoquad-pol data, results show that the cross-pol reconstruction performance of the “π/4” mode is much worse than that of the “CTLR” mode, with a bias of 2.161 dB and an rms error of 2.356 dB. Fig. 9 shows a quad-pol SAR image of natural oil slicks in the Gulf of Mexico acquired at 12:09 UTC on June 9, 2015. The radar incidence angles are between 28.1° and 29.8°. There is an NDBC buoy (#42001) located close to the image domain. The buoy-measured wind speed is 3.1 m/s. Oil slicks dampen the capillary and small gravity waves generated by local winds and thus reduce the radar backscatter from the ocean surface, resulting in the dark areas in this SAR image. Because the co-pol signal intensities are much stronger than those of the corresponding cross-pol, the co-pol image contrast, between the clean ocean surfaces and the oil slick-covered regions, is larger than that of cross-pol result. As a result, the oil slicks can be seen more clearly in the co-pol SAR image than in the cross-pol SAR image. The NESZ minimum of the SAR image shown in Fig. 9 is −37.7 dB. We can identify the oil slicks in Fig. 9(a), because the cross-pol backscatters from oil slicks are still above the NESZ. In this case, we apply the “CTLR” mode methodology, with variable N to construct the pseudoquad-pol data. In this case, N is also not assumed to be a constant (of value 4) because (9) is not a strict equality and is not a very good fit to the data, as shown in Fig. 10. Most of the data lies above the equality line, indicating that the value of 4 in the denominator is close to a lower limit and that the most of the N values are higher than 4. The reconstructed pseudoquad-pol data are shown in Fig. 11; they are similar to the original quad-pol data illustrated in Fig. 9. Fig. 12 shows that the reconstructed and the original co-pol backscatters are in good


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Fig. 11. Reconstructed pseudoquad-pol data (a) |SHH |2 , (b) |SVV |2 , and (c) |SHV |2 from original quad-pol data, as shown in Fig. 9, with the “CTLR” mode algorithm (N = |SHH − SVV |2 /|SHV |2 ).

Fig. 12. Scatter plot of the original quad-pol data shown in Fig. 9 versus the reconstructed pseudoquad-pol data shown in Fig. 11. (a) for HH, and (b) for VV, and (c) for HV.

Fig. 13. (a) Scatter plot of the two sides of (9). The diagonal line indicates where data would lie if (9) was a strict equality. Plotted on the abscissa and ordinate are |SHV |2 /(|SHH |2 + |SVV |2 ) and (1 − |ρ|)/4, respectively, which are derived from the original collocated quad-pol data set. (b) Scatter plot of the original HV-polarized backscatters from the collocated data set versus those reconstructed values from the “CTLR” mode algorithm (N = |SHH − SVV |2 /|SHV |2 ).

agreement. Even for the cross-pol reconstruction data, the bias and rms error are small: 0.095 and 0.419 dB, respectively. The reconstructed cross-pol backscatters are larger than NESZ, which are shown in Fig. 11(c). We also apply the “π/4” mode and the estimated Nvalues to construct the pseudoquadpol data. The reconstructed cross-pol data show a bias of −0.534 dB and an rms error of 0.627 dB. In addition to case studies, as shown in Figs. 3, 7, and 11, we also used the collocated data set to assess the cross-pol backscatter reconstruction performance. The data set included 1594 pairs of incidence angle, quad-pol covariance matrix elements [|Shh |2 , |Shv |2 , |Shv |2 , Shh (Svv )∗ ] and

2 |), and the left- and right-hand sides N (|SHH − SVV |2 )/|SHV of (9). As pointed out in [4], N is the ratio of double-bounce backscatter to volume backscatter. Fig. 13(a) shows that the left-hand side of (9) is not strictly equal to the right-hand side. Thus, we used the “CTLR” mode reconstruction algorithm and N values to construct the cross-pol backscatter. The comparison between reconstructed and original cross-pol backscatters is shown in Fig. 13(b). The bias and rms error are 0.099 and 0.485 dB, respectively. We also examine the dependence of cross-pol reconstruction performance on wind speeds and radar incidence angles. The wind speeds and incidence angles are first divided into different bins,

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Fig. 14. Scatter plot of the original HV-polarized backscatters from the collocated data set versus those reconstructed values from the “CTLR” mode algorithm (N = |SHH − SVV |2 /|SHV |2 ) for (a) 20o < θ < 35o , (b) 35o < θ < 50o , (c) 1 m/s < wind speed < 8 m/s, and (d) 8 m/s < wind speed < 16 m/s.

Fig. 15. Reconstructed pseudoquad-pol data (a) |SHH |2 , (b) |SVV |2 , and (c) |SHV |2 from original quad-pol SAR image acquired at 11:52 UTC on April 23, 2011.

and then cross-pol backscatters are reconstructed by using “CTLR” mode and the estimated N values. We compare the reconstructed cross-pol returns in each bin with the original radar observations, which are shown in Fig. 14. The results show that the reconstruction accuracy deteriorates when incidence angle and wind speed increase. B. Oil Platform Detections and Verifications As mentioned in Section III-C, the relative phase is a good indicator to discriminate the odd scattering events, from the

multiple scattering events, therefore suggesting a potentially unsupervised classification methodology. In this paper, we collected 29 C-band RADARSAT-2 quad-pol SAR images acquired between April and August 2010 over the Gulf of Mexico, and constructed the corresponding pseudoquadpol data, using these original quad-pol SAR images. The reconstructed co- and cross-pol backscatters are first used to estimate relative phase for oil platform detections. Subsequently, we use the BOEMRE database to validate the SAR oil platform detections.


Fig. 16. Estimated relative phase from reconstructed pseudoquad-pol data, as shown in Fig. 15.

Fig. 17. Oil platform mapping derived from the relative phase shown in Fig. 16. The oil platform locations from BOEMRE database are also included.

As a case study, Fig. 15 shows the reconstructed pseudoquad-pol data from a C-band RADARSAT-2 fine quadpol SAR image of the Gulf of Mexico acquired at 11:52 UTC on April 23, 2011. We show the estimated relative phase values in Fig. 16. The relative phases are negative for the oil platforms, whereas they are positive for the clean ocean surface areas. We apply the relative phase as a filter to map the oil platforms. In this case, according to the geographic locations of oil platforms reported by BOEMRE, there are 75 oil platforms (red circles in Fig. 17) in the region covered by the SAR image. Fig. 17 shows that the positions of 58 SAR-detected oil platforms (dark points) coincide with

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the locations in the BOEMRE records. Moreover, the locations of three detected oil platforms are not present in the BOEMRE database. They are marked by blue boxes in Fig. 17, which are corresponding to false detections. Note that 14 oil platforms (red circles without dark points in Fig. 17) are not detected, because they are not found in the co- and cross-pol SAR images, as shown in Fig. 15; however, these platforms are recorded by BOEMRE. The missing detections are possibly caused by the changes in oil platform positions due to hurricanes, or the platforms had been simply removed by oil companies. In addition to the case study, we also detect the oil platforms using the above-mentioned 29 SAR images acquired between April and August 2010 to further validate the proposed detection method. These SAR images are shown in Fig. 18(a). Fig. 18(b) shows the geographic positions of all oil platforms in the Gulf of Mexico provided by BOEMRE. In the coverage of 29 SAR images, there are 878 oil platform positions in the BOEMRE database, which are illustrated together with 747 CP SAR detections, as shown in Fig. 18(b). Of these detections, 687 detected oil platform locations are identical with these in the BOEMRE records. The remaining 60 targets are possibly associated with other metallic objects (stationary or moving ships). Moreover, the positions of 131 oil platforms have changed due to rig movement or hurricane-induced damage. The percentage of correct detection is 78% assuming that no any other oil platforms changed their positions during the time interval between the completion of the BOEMRE records and SAR image acquisitions. As a matter of fact, the percentage should become larger without any assumptions, because at least some oil platforms moved to new positions or sank due to hurricanes. Both fixed and mobile offshore oil platforms are used in the Gulf of Mexico. Mobile oil platforms can move to new locations from their original positions, in which case, SAR cannot always detect them, because their locations have changed. Hurricanes have the capability to move oil platforms to new locations and even inflict serious damage. According to a report from the United States Department of State, Hurricane Katrina (2005) destroyed and sank 115 oil platforms, significantly damaged 52, and set adrift 19 (http://www.state.gov/documents/organization/ 150082.pdf). Therefore, the proposed SAR CP detection method is very helpful for rapid identification of the existence of changes in positions in the locations of oil and gas platforms in the Gulf of Mexico after hurricane events. C. Oil Slick Detections and Verifications Previous and recent studies show that the Bragg scattering is associated with both the slick-free and the weak-damping slick-covered sea surface [45], [55]. Surfactants, such as biogenic films have weak damping properties, are still associated with Bragg scattering, as in the case of the clean sea surface [56]. Airborne L-band UAVSAR DWH oil slick analysis results suggest that the Bragg scattering is dominant in the oil slick-covered areas [40], which is possibly attributed to weak-damping feature and the state of weathering. However, experiments with both airborne and spaceborne

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Fig. 18. (a) 29 original C-band RADARSAT-2 quad-pol SAR images acquired in the Gulf of Mexico. (b) Oil platform mappings derived from relative phase estimated by reconstructed pseudoquad-pol data from SAR images shown in (a). The oil platform positions recorded by BOEMRE are also shown using red circles.

L-, C-, and X-band SAR images have demonstrated that nonBragg scattering should be the dominant mechanism for oil slicks with strong damping [56]. Moreover, a larger number of studies have concluded that non-Bragg scattering mechanism is dominant when dealing with strong-damping oil-covered sea surface [35], [37], [53], [57], [58]. In this paper, natural oil slicks are also detected using the proposed method. Before making the tests for oil slick detection, we first analyze the scattering mechanisms in clean sea surface and slick-covered regions with polarimetric decomposition parameters (entropy and alpha). Fig. 19(a) and (b) shows the entropy and alpha estimated by a C-band RADARSAT-2 quad-pol SAR image. As shown in Fig. 19(a), high entropy values exist in the slick-covered areas, which demonstrate that the capillary and the small gravity wave are strongly dampened by oil slicks. Backscatters from slick-covered areas are dominated by non-Bragg scattering. For the clean sea surface, the dominant surface Bragg scatter-

ing results in low entropy values. Alpha is also an important polarimetric decomposition parameter characterizing different scattering mechanisms. For alpha values below 30°, surface Bragg scattering dominates; for ∼30° < alpha < 50°, dipole scatter dominates; and for ∼50° < alpha < 90°, multiple scatter dominates. The alpha values for oil slicks shown in Fig. 19(b) are larger than 50°, suggesting that non-Bragg scattering (multiple scattering) is dominant. By comparison, the clean sea surface has alpha values