IMPROVED OIL SLICK DETECTIONAND CLASSIFICATION WITH POLARIMETRIC SAR

J. Fortuny-Guasch

EC DG Joint Research Centre, I-21020 Ispra (Va), ItalyTel.: +39 0332 785104, Fax: +39 0332 785469, E-mail: Joaquim.Fortuny@jrc.it

Abstract

A study on the potential of space-borne polarimetric synthetic aperture radar (SAR) imagery for an improved detectionand classification of oil spills is presented. An image data set consisting of five SIR-C/X-SAR acquisitions over the NorthSea, English Channel, and Southern Italy is used. Results show that two land surface roughness indicators (i.e., thecircular polarization coherence (CPC) and the polarimetric anisotropy) can also be used to identify oil spills both in Land C-Band imagery. The polarimetric anisotropy is shown to be a quite robust indicator of oil slicks on the sea surface.

1 Introduction

Ocean oil pollution has now become a major environmental concern in the world. There are two main sources of oilpollution in oceans [1]. First, the operational tanker oil discharges (i.e. dumping of oil during tanker cleaning operations),which form about half of the total ocean oil pollution in the world. Secondly, the ship accidents and oil platform accidents,which contribute about a 10% to the total ocean pollution. Hence, illicit discharges from ships impose a much greaterlong-term threat to the ocean environment than that from big tankers accidents. Monitoring illegal discharges is thusan important component in ensuring compliance with the marine protection legislation and the general protection of thecoastal environments. With various earth-observation satellites available today it is now possible to implement an effectivestrategy on the global monitoring of ocean oil pollution. Space-borne synthetic aperture radar (SAR) is today an importanttool in ocean oil slick monitoring due to its wide coverage with day and night all-weather capability.

As an example, the use of ESA/ERS radar imagery has been proposed as an effective tool for the monitoring of illicitdischarges in the Mediterranean Sea [2], in the North Sea, and in South Eastern Asia [3]. This task has associated theanalysis of a large amount of SAR scenes. Some unsupervised spill identification schemes have been developed in orderto ease the task of image analysis and interpretation [4, 5]. However, these schemes are still far from being operational andfurther research is needed. The effectiveness of these unsupervised or automatic identification schemes may substantiallybenefit from the availability of multi-polarization images (e.g., those acquired by ENVISAT).

The future advent of polarimetric SAR Earth observation platforms (e.g., RADARSAT-2, ALOS, TERRASAR-X) posesthe question whether there will be any improvement in the performance of the oil slick detection and classification schemescurrently used with single-polarization imagery. This is seen as an important issue to be addressed now when various spaceagencies are defining their future missions. The assessment of this potential improvement can be done analyzing someexisting polarimetric data sets. As an example, some full polarimetric space-borne acquisitions over various oil slickstook place in April and October 1994 during the NASA/JPL SIR-C/X-SAR Mission.

In this paper, the use of the circular polarization coherence (CPC) and the polarimetric anisotropy as potential indicators ofthickness and/or texture changes of the oil film has been addressed. These two measurables have been recently proposedas estimators of land surface roughness, therefore their potential use in an oil slick detection and classification schemeneeds to be assessed.

Keywords: Polarimetric SAR, oil slick, anisotropy, circular polarization coherence, surface roughness..

2 Theory

The sensitivity of the radar backscatter to surface roughness is well known qualitatively. Surface roughness is itself animportant information required in different application areas such as oceanography, agronomy, pedology, weather andclimate forecasting, and geology. It is well known that SAR imagery is extremely sensitive to the presence of oil films in

Figure 1 - Scattering mechanisms of an oil slick.

the sea. The high sea reflectivity is due to Bragg scattering from capillary and short gravity waves. For that reason, thebackscatter intensity in VV is stronger than that in HH. Slicks attenuate the short gravity waves and reduce the backscatterintensity acting as a reflectors, as shown in figure 1. The detection of slicks with single-polarization (VV) space-borneSAR has found to be optimal at small incidence angles and under moderate wind speeds (say in the range 3-8 m/s).One would expect that these validity ranges can be extended if additional images in different polarization channels wereavailable. In that context, the optimal sensor would be a fully polarimetric space-borne SAR.

The imagery used was acquired within the frame of the SIR-C/X-SAR Mission in April and October 1994. A total of fiveMulti-Look Complex (MLC) fully polarimetric images with both L and C-Band data were used. The available images inX-Band were quick-looks and therefore no polarimetric analysis could be applied to those.

The purpose of this paper is to investigate the effect of surface roughness on polarimetric SAR data acquired by the SIR-C/XSAR system with the aim of identifying the presence of oil slicks. To this end, two surface roughness indicators havebeen used. First, the CPC, an then, the polarimetric anisotropy derived from the Cloude/Pottier classification scheme [6].

The definition of the CPCρrrll is as follows:

ρrrll =< Srr · S∗

ll >√< Srr · S∗

rr >< Sll · S∗ll >

(1)

wherein

Srr =Shh − Svv + 2jShv

2(2)

Sll =Shh − Svv − 2jShv

2. (3)

In 1997, Cloude and Pottier developed a useful eigenvalue/eigenvector polarimetric decomposition which allows calcula-tion of the polarimetric anisotropy [7]. The anisotropy is calculated from the using the second and third eigenvalues of apolarimetric decomposition algorithm. The anisotropy is defined by

A =λ2 − λ3

λ2 + λ3(4)

whereinλ1, λ2, λ3 are the eigenvalues, ordered from largest to smallest, of the coherency matrix[T ], which is defined as

[T ] =12

<

(Shh + Svv)(Shh + Svv)∗ (Shh + Svv)(Shh − Svv)∗ 2(Shh + Svv)S∗hv

(Shh − Svv)(Shh + Svv)∗ (Shh − Svv)(Shh − Svv)∗ 2(Shh − Svv)S∗hv

2Shv(Shh + Svv)∗ 2Shv(Shh − Svv)∗ 4ShvS∗hv

> (5)

Humanitarian Security UnitInstitute for the Protection and Security of the Citizen

6

Study Areas (1)

English Channel

Figure 2 - X-Band quick-look image acquired over the English Channel.

Figure 3 - Images of the total power (left) and the CPC (right) corresponding to the scene over the English Channel (C-Band).

It is worth noting that the two above roughness indicators do, however, become the same in the special case of azimuthsymmetric scatter, where the hermitian coherency[T ] matrix is diagonal.

3 Results

A total of five images with full polarimetric SAR data acquired were used in this assessment. The study sites includedthe North Sea, the English Channel, Northern Germany, and the Pantelleria Island in Southern Italy. A summary of theresults obtained using the two surface roughness retrieval schemes is next.

Figure 2 shows the quick-look image over the English Channel. The region of interest is indicated with a blue rectangle.The images of the total power and the CPC are shown in figure 3. These images are those corresponding to the C-Band.It can be clearly seen that, as expected, the slick area shows a lower reflectivity compared to rest of the image. It alsocan be seen that the CPC within the slick is lower (i.e., a smoother surface). Similar results with the CPC have also beenobserved in the other study areas.

Figure 4 shows the quick-look image over the North Sea. In this scene there are three oil slicks that were dumped onpurpose just before the image acquisition. This was an experiment organized by the University of Hamburg. These slicksare clearly distinguishable because of their unusual shape (square). There was a large slick in the center and two smallones on the sides. The images of the total power and the polarimetric anisotropy are shown in figure 5. These imagesare those corresponding to the C-Band. It can be clearly seen that, as expected, the slick area shows a lower reflectivitycompared to rest of the image. It also can be seen that the polarimetric anisotropy is much higher within the slick. In thisimage, there was also a ship next to the center slick, which shows an extremely low anisotropy.

Finally, figure 6 shows the quick-look image over the Pantelleria Island (Southern Italy). In this scene there is an oilslick located South of the Pantelleria Island. The associated images of the total power and the polarimetric anisotropy areshown in figure 7. These images are those corresponding to the L-Band. Again, the slick area shows a lower reflectivitycompared to the surrounding sea. It also can be seen that the polarimetric anisotropy is uniquely high within the slickand nowhere else. Note that this scene also includes the southern part of the Island where there is an airport. One would

Humanitarian Security UnitInstitute for the Protection and Security of the Citizen

7

Study Areas (2)

Westerland (Northern Germany)

Figure 4 - X-Band quick-look image acquired over the North Sea.

Figure 5 - Images of the total power (left) and the polarimetric anisotropy (right) corresponding to the scene over the North Sea(C-Band).

Figure 6 - X-Band quick-look image acquired over the Pantelleria Island.

Humanitarian Security UnitInstitute for the Protection and Security of the Citizen

16

Pantelleria Island, L-Band Data

Anisotropy Changes withinthe Oil Slick

Figure 7 - Images of the total power (top) and the polarimetric anisotropy (bottom) corresponding to the scene near the PantelleriaIsland (L-Band).

expect that flat surfaces as those of the landing lanes could also show a high anisotropy. However, this is not the case andthis result indicates that, in this very image, the polarimetric coherence is a robust oil indicator.

4 Concluding Remarks

An assessment on the potential use of two surface roughness retrieval schemes for an improved detection and classificationof oil slicks was presented. The imagery used consisted of five full polarimetric data sets acquired during the SIR-C/X-SAR missions in April and October 1994. The analysis of the imagery showed that the anisotropy is always higher withinthe slick area both in L and C-Bands. Moreover, the estimates given by the circular polarization coherence (CPC) wereconsistent (i.e., a smaller roughness within the slick area).

The presented results are preliminary and further research needs to be focused on two issues. First, the establishmentthe new applicability range (in terms of wind intensity and incidence angle) and robustness of the proposed polarimetricidentification schemes. Second, the identification of alternative slick indicators for ENVISAT dual-polarized products(HH/VV, HH/HV, VV/HV) based on the findings presented in this paper.

References

[1] “Oil pollution monitoring, in ERS and its applications: Marine”. ESA Publications BR-128, vol. 1, 1998.

[2] Pavlakis, P., Sieber, A., and Alexandry, S., 1996. “Monitoring oil-spill pollution in the Mediterranean with ERSSAR”. Earth Observation Quarterly, Nr. 52.

[3] Jingxuan, L., Hock, L., Liew, S. C., Bao, M., and Kwoh, L. K., “Oil pollution statistics in Southeast Asian waterscompiled from ERS synthetic aperture radar imagery”. Earth Observation Quarterly, Nr. 61, 1999.

[4] Del Frate, F., Petrocchi, A., Lichtenegger, J., and Calabresi, G., “Neural Networks for Oil Spill Detection UsingERS-SAR Data”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 38, No. 5, September 2000, pp. 2282-2287.

[5] Schistad Solberg, A. H., Storvik, G., Solberg, R., and Volden, E., “Automatic Detection of Oil Spills in ERS SARImages”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 37, No. 4, July 1999, pp. 1916-1924.

[6] Schuler, D. L., Lee, J. S., Kasilingam, D., and Nesti, G., “Surface Roughness and Slope Measurements Using Polari-metric SAR Data”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 40, No. 3, March 2002, pp. 687-698.

[7] Cloude, S. R. and Pottier, E., “An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR”,IEEE Trans. on Geoscience and Remote Sensing, Vol. 35, No. 1, January 1997, pp. 68-78.