OIL SPILLS AUTOMATIC DETECTION FROM SAR IMAGES

OIL SPILLS AUTOMATIC DETECTION FROM SAR IMAGES

F. Nirchio1, S. Di Tomaso1, W. Biamino2, E. Parisato2, P. Trivero2, A. Giancaspro3

(1) ASI, Geodesy Space Centre, Loc. Terlecchia, 75100 Matera, Italy. francesco.nirchio@asi.it

(2) Università degli Studi del Piemonte Orientale ‘Amedeo Avogadro’, Dipartimento di Scienze dell’Ambiente e della

Vita, Via Bellini 25/g, 15100 Alessandria, Italy. trivero@unipmn.it

(3) Telespazio, Geodesy Space Centre, Loc. Terlecchia, 75100 Matera, Italy. antonio_giancaspro@telespazio.it

ABSTRACT

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in Synthetic Aperture Radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas under test. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. The system and its ability to detect and classify oil and non-oil surface features are described. The ERS probability to detect an oil pollution event is estimated using a set of verified oil spills and analysing the wind intensity, deduced from the image itself. The present performances of an off line processing data centre are also analysed, in term of throughput and response time, in order to verify them against the user requirements.

1 INTRODUCTION

A great aid in the effort of development of an oil spill operational monitoring system comes from remote sensing techniques. Among all the monitoring ways, the Synthetic Aperture Radar (SAR) is a powerful tool for the detection of slicks caused by oil-tanker accidents, leakages from oil platforms and pipelines and by large amounts of oil deliberately released through the discharge of bilge and ballast waters. In the SAR images a slick appears as dark area because of reduction of water surface roughness. During the last years, airborne and space-borne SAR data availability allowed us to make several SAR application studies on artificial or natural slicks, confirming the ability of the SAR to monitor sea surface slicks. Results of experiments performed in the Mediterranean Sea, with microwave remote sensing systems [1], prove that it is possible to implement an automatic overseeing system able to give important information useful for determining physical properties, origin, total volume discharged and the motion of the slicks.

Some classification algorithms for oil spill detection have been proposed using pattern recognition techniques [2], frequency spectrum damping measurements [3], [4] and neural networks recognition techniques [5]. In recent years a probabilistic approach was developed and tested to distinguish oil spills from other similar oceanic features in marine SAR images [6], [7]. The method, using statistical information obtained from previous measurements of physical and geometrical characteristics for both oil spill and natural features, can discriminate between oil spills and other natural phenomena that cause backscattering attenuation. This method, improved by using a new classification algorithm for oil spill identification in low-resolution images, is now operative and represents a useful step to a completely automatic monitoring system.

2 SEA SURFACE SLICKS MEASURED BY SAR

The presence of oil at water surface changes the wind stress on the sea surface and reduces the waves amplitude. Measurements of slick-induced damping of short-gravity ocean waves excited by the wind provide useful data for the investigation and characterization of ocean slicks on a thermodynamic basis [8]. T he theory of the rheology of air-water interface [9], [10] predicts

a maximum of the ratio between the damping coefficients of waves for water covered by a surface film and pure water. The mathematical analysis by Levich [11], based upon the Navier-Stokes equation and developed in the case of small ripples on an interface covered by a surfactant substance, has been extended by Cini et al. [12] including both soluble and insoluble films for the two coexisting modal solutions: the Laplace or transversal mode and the Marangoni or longitudinal mode [13]. Spectral measurements carried out both in tanks and in many oceanic sites [3], [14], [15], [16] on slicked waters clearly show this damping effect, which causes the measurable attenuation of backscattered signal that forms the SAR imagery. The ratios between spectra measured in pure water and in water covered by film have a maximum in the 3-10 Hz region [8], [17].

The SAR is a useful instrument to monitor sea surface substances, since SAR data contain information about the spectral components affected by damping. The basic

_____________________________________________________ Proc. of the 2004 Envisat & ERS Symposium, Salzburg, Austria

mechanism involved is the Normalized Radar Cross-Section (NRCS) which, for incidence angles higher than 20°, is proportional to the spectral energy density of the sea waves having wavelength Λ that obey the Bragg resonance condition:

angle of the radar beam. For low incidence angles the backscatter is due to specular reflection [18]. The sea waves, which are Bragg resonant with microwaves, employed by radar systems, fall in the short gravity wave region.

The detection and characterization of substances forming sea surface films using multi-frequency SAR was suggested by Fiscella et al. [19]. Today airborne and space borne SAR availability has allowed significant experimentations on slicks [4], [20], [21], [22]. To prove SAR capability in monitoring sea surface films, a first experiment, with an artificial spreading slick of oleyl alcohol took place in October 1990 in the northern Adriatic Sea, offshore the Venice coast in the area around the oceanographic platform "Acqua Alta" of the Italian National Research Council (CNR). The platform was equipped with a three-frequency radar scatterometer (in L-, S- and C-band) and a specially designed interferential microwave probe which measures wave heights on absolute self-calibrating scale. The measurements of water height can be accomplished with an accuracy of the order of few micrometers from 0 to 20 Hz. Two small spots (~ 104

m2 each) were produced on the sea surface upwind the oceanographic platform at approximate distances of 1 km and 100 m, respectively, using in all 3.5 litres of oleyl alcohol. Carried by surface current, one of the spots crossed the tower permitting us to measure the wave damping by the three-frequency scatterometer and by the wave gauge installed on board. The ratios between the mean values of the NRCS at the various bands, outside the damp region and the minimum value inside, are compared with ratio between spectral data measured by microwave interferential probe, demonstrating a good agreement [4].

In June 1991 in the Gulf of Genoa analogous experiment was performed observing slicked area after oil-tanker accident [1]. During the flight we performed sea truth measurements either from shore or from a small boat. The SAR images display many slicks that were also directly observed from shore and boat. The intensity decreases regularly from near to far range by about 10 dB. Ground-based measurements consisted of standard meteorological observations. On boat measurements included wind observations and time series of high-resolution sea surface elevation with the microwave probe. For the SAR scenes we have three VV polarization images respectively for the P-, L- and C-band. We elaborated each band of the images by excluding the regions showing land, radar interference and vessels tracks. By following the hypothesis that the Bragg mechanism is the main contribution to the radar backscatter and by associating to each range position the corresponding incidence angle θ, equation (1) gives the wavelength of the water surface wave component responsible for backscatter. From each range position we explored the image along the azimuth direction and plotted the pixel intensities at the same Bragg frequency. From the spectral shape analysis the upper

points of the clusters show a continuous trend resembling the one obtained with the probe in rippled sea areas, while the bottom edge of the clusters almost match the damped sea waves spectrum. By plotting the ratio between upper limit and lower limit of clusters we obtain a very similar to the spectral ratio from gauge data.

In Fig. 1 we resume the comparison between in situ measurements of spectral ratio obtained with interferential probe and the remote sensing values obtained from the two campaigns.

Fig. 1. Correlation between in situ and remote

sensing measurements on oil slicks

3 OIL SPILL DETECTION

With the purpose of distinguishing oil spills from other similar oceanic features, in the last years we have developed a method, named ‘Oil Spill Automatic Detector’ (OSAD) [6], [7].

The OSAD algorithm, written in IDL language, is able to analyse an ERS image in real time: computing time is about 3 minutes for a PRI image on a dual 500 MHz Pentium III PC. However, the production time for a PRI image is about two hours after data acquisition.

OSAD system is implemented in Matera Geodesy Space Centre, where the Italian Space Agency Processing and Archiving Facility (PAF) for the European Remote Sensing (ERS) satellite sensor data and the ‘Telespazio’ acquisition facility are located. Here, raw data are acquired directly from ERS satellite by means of an 8m main dish; after acquisition, data are saved on a storage media and the analysis process starts.

A detailed description of the whole process, from satellite acquisition up to the generation of a detection report, can be found on [7].

The block diagram in Fig. 2 shows the entire detection process.

Fig. 2. Block diagram of the detection process

Two sets of samples of oil spills and look-alikes were analysed (see Fig. 3 and Fig. 4, for example): a model was built for both cases. Every potentially polluted area is compared to the models to classify it.

Fig. 3. Example of oil spill in SAR image

Fig. 4. Example of a look-alike feature

The experimentation was conducted using high-resolution products (Precision Image Product (PRI) in ESA language) of ERS 1 and 2 satellites. The images are calibrated [23] to retrieve the radar backscattering, and the attenuation due to the incidence angle is compensated [24]; in fact, the sea scattering is dominated by the Bragg mechanism (see Eq.1), which produces a stronger signal in the near range then in the far range. The selected samples, 390 in total 237 oil spills and 153 look-alikes, have been chosen by an expert photo-interpreter.

The following measurements have been performed: Area: the extension of the surface interested (A) Perimeter (P)

Average backscattering inside the area (ABIA) Standard deviation of the backscattering inside the area (SDBIA)

Average backscattering outside the area (ABOA) Standard deviation of the backscattering outside the area (SDBOA)

Form Factor: the regression factor of the line passing inside the dark area along the main axis (FF)

From the previous ones the following parameters are computed:

Ratio between area and perimeter (AP)

Ratio between average backscattering inside and outside the area (RBIO)

Ratio between average backscattering and its standard deviation inside the area (RBSDI)

Ratio between average backscattering and its standard deviation outside the area (RBSDO)

Ratio between backscattering standard deviation inside and outside the area (RSDIO)

Ratio between RBSDI and RBSDO (RBSDIO)

Using the two datasets we identified those parameters which can distinguish between the two groups (of oil spills and look-alikes) and subsequently set up a procedure to classify the new cases into one group or into the other.

We adopted a multi regression approach (or a Fisher discriminant analysis) to set up a relation between the predictor variables and the dependent or criterion variable on a dataset containing 153 verified oil spills and 237 look-alikes [7].

The method assures a correct classification better then 90 %. The inclusion into the model of other variables, like the polarization, should increase the discrimination capability.

Table 1 reports the cases classified by the interpreter on the row while those classified by the algorithm on the column.

Photo interpreter

Oil Non oil % Oil 144 9 94

Non oil 29 208 88A l g o r i t h m

Total 173 217

90

We tested the model adopted for the classification, derived by PRI data, to images with different radiometric and geometric resolutions. We derived a model expression for images with pixel spacing of 25 m, 50 m and 100 m using a set of 15 samples of dark areas and computing all the parameters in the model at

different resolutions.

We have found that the NRCS average values were substantially similar to the PRI results; there was a

difference < 2%, for images with pixel spacing of 25 and 50 m, while the difference were within 10% for the

images with 100 m of pixel spacing. The form factor

(FF) was also affected by the different resolutions. Its variation is < 10% for images at 25 and 50 m of pixel spacing, while it is larger for images at 100 m of pixel spacing. This depends also on the overall area extension under test, the smaller the areas, the more relevant the border effects.

Differently from the previous results, the NRCS standard deviation presented values more or less consistent with the expected ones only outside the dark areas (over the clean sea), while inside it the values were considerably larger than those expected.

Due to these results we have considered a different model, which do not take into account the standard deviation inside the dark area, but can be also applicable for images at poor resolution. It includes the following primary measurements: ABIA, ABOA, FF, SDBOA; and has an a posteriori probability of correct classification of 88%. The classification capability in this case is reduced in comparison to the full resolution data.

4 SAR CAPABILITY TO DETECT OIL SPILL

A set of data of oil spills sighted between February and October 2000, by the Coast Guard has been given to us by the Italian Ministry of the Environment. For each pollution episode, location (latitude and longitude) and time were given. Most oil spills have been sighted in proximity to the coast and fewer offshore.

The developed procedure to verify the SAR capability in detecting oil spills consisted in the following steps:

? Of each polluted site a research has been made

on the on-line ESA catalogue in order to find a SAR image (SAR quicklook with pixel spacing of 200 m) that would take the scene in study. ? Wind fields have been used provided by the

QuickScat satellite relative to the SAR images found previously, to evaluate the wind influence in the capability of detecting oil spills.

? The SAR images have been georeferenced and

subsequently analysed.

? SAR images have been compensated for the

incidence angle.

? Images were obtained integrating wind data

(intensity and direction) and average intensity of the SAR image.

Fig. 5 and Fig. 6 show two example of oil spill and look-alike respectively.

Fig. 5. Example of detected oil spill

Fig. 6. Example of not detected oil spill

On 6 out of 23 polluted sites the research on ESA catalogue did not provide the SAR images necessary for the detection of the oil spills reported.

Of the remaining 17 polluted sites, in 6 cases the oil spills were sighted at a distance greater than 5 km offshore. In 4 of these cases it was possible to detect them with the SAR images.

In the remaining sites, the oil spills were sighted near the coast. The detection was possible only in 2 cases. The possible causes of unsuccessful detection in the remaining cases can be attributed to the wind sheltering effect, the strong winds (over 10 ms-1), the presence of natural film, the excessive proximity to the coast and the low resolution in the images utilized.

From the analysis carried out on the polluted sites the following conclusions were drawn.

In the cases of the detection of oil spills, the wind intensity on the sea has always been between 2 ms-1 and 10 ms-1;

An important factor in detecting oil spills was proved to be the distance from the coast: in fact in the near-shore region the detection percentage drops quickly, because in these cases the effect of wind sheltering becomes determinant, that is, the wind screening effect caused by the local topography on the areas near-shore. This effect creates SAR images with dark areas near-shore, hindering the correct detection of oil spills.

5 EXTENSION TO ENVISAT DATA OF THE PROPOSED APPROACH.

The availability of new observation capabilities, first of all ENVISAT, encourage us to extended the approach adopted for ERS also these data, of course it is essential to take into account the differences among the missions.

Fig. 7. ENVISAT acquisition on Atlantic Ocean ENVISAT is particularly attractive, from the point of view of oil spill monitoring, because it has several different acquisition modes, some of them able to cover areas up to 400 times 400 km. In this case the incidence angle ranges from 16° to 42° and the scattering mechanisms is not only the Bragg one, but also the specular one. The compensation for the incidence angle, used in the ERS case, and based on the assumption of a Bragg scattering, is not appropriate. In order to estimate this compensation, a wide swath ASAR image over the ocean has been selected; the image quick look is shown in Fig. 7. Particular attention has been paid in order to have an image with uniform intensity along the azimuth.

Fig. 8. Mean energy along the image

Image corners are:

First near latitude: 28.65

First near longitude: -16.11

Last far latitude: 25.41

Last far longitude: -21.15

Polarisation: VV

The image is a portion of Atlantic ocean near the Canary islands; the lower part (land free) has been used for the analysis.

A set of one hundred range lines have been selected, averaged and the maximum value normalized to 1, as shown in Fig. 8.

A fit has been made with an exponential function. The analytical expression we have got is reported in the following.

F(x)= 19.4*exp(-0.18x)+0.06 (2)

The other differences, like the geometric and the radiometric resolution, are presently under evaluation and will be part of a future research project.

Some of these new developments have been funded by a IST EU contract called DISMAR. The overall objective of DISMAR (Data Integration System for Marine Pollution) is to develop an advanced (intelligent) information system for monitoring and forecasting marine environment to improved management of pollution crises in coastal and ocean regions of Europe, in support to public administrations and emergency services responsible for prevention, mitigation and recovery of crises such as oil spill pollution and harmful algal blooms (HAB).

CONCLUSION

We developed a methodology able to detect in an automated way oil spills from SAR images and to distinguish between oil spills and look-alike, providing the probability of being an oil spill. The potentiality and limits in the use of SAR images in the identification of oil pollution are verified using a dataset of verified cases. Oil spills can be detected from the analysis of SAR images, also using a low resolution, if the wind speed results between 2 ms-1 to 10 ms-1; the SAR’s capability in detecting oil spills has given good results at open sea even using a low resolution, while in sheltered areas the effects that limit its potentiality become determinant.

The same approach is extended to ENVISAT date, taking into account the differences among missions, in particular incidence angles; the possibility to use different polarizations will increase the discrimination capability of oil spill and ship responsible of spilling. ACKNOWLEDGMENTS

This work has been conducted in a research project selected by ESA in the framework of the ERS A.O.3. All the data used are ERS and ENVISAT data on which there is an ESA copyright. The data processing has been carried out at Italian PAF. The extension of the methodology to ENVISAT has been co-founded by EU with the project DISMAR, contract N. IST-2001-37657. We want to thank the Italian Environment Ministry, Dept. of marine resources protection, for the data provided of the identified oil spills.

REFERENCES

1. Trivero P., Fiscella B., Gomez F. and Pavese P., SAR detection and characterization of sea surface slicks, International Journal of Remote Sensing, 19, 543-548, 1998.

2. Fukunaga K., Introduction to statistical pattern recognition, 2nd edition (New York: Academic Press), 1990.

3. Lombardini P.P., Fiscella B., Trivero P., Cappa C. and Garrett W.D., Modulation of the spectra of short gravity waves by sea surface films: slick detection and characterization with a microwave probe, Journal of Atmospheric and Oceanic Technology, 6, 882-890, 1989.

4. Trivero P., Fiscella B. and Pavese P., Sea surface slicks measured by SAR, Nuovo Cimento, 24 C, 99-111, 2001.

5. Calabresi G., Del Frate F., Lichtenegger I., Petrocchi

A. and Trivero P., Neural Networks for the oil spill detection using ERS-SAR data, IGARSS ’99 Workshop, 1, 215-217, 1999.

6. Fiscella B., Giancaspro A., Nirchio F., Pavese P. and Trivero P., Oil spill detection using marine SAR images, International Journal of Remote Sensing, 21, 3561-3566, 2000.

7. Nirchio F., Sorgente M., Giancaspro A., Biamino W., Parisato E., Ravera R., Trivero P., Automatic detection of oil spill from SAR images, International Journal of Remote Sensing, in press.

8. Cini R., Lombardini P.P. and Hühnerfuss H., Remote sensing of marine slicks utilizing their influence on water spectra, International Journal of Remote Sensing, 4, 101-110, 1983.

9. Cini R. and Lombardini P.P., Damping effect of monolayers on surface wave motion in a liquid, Journal of Colloid and Interface Science, 65, 387-389. 1978. 10. Hühnerfuss H., The molecular structure of the system water/monomolecular surface film and its influence on water wave damping, Habilitation thesis, University of Hamburg, 245. Hamburg, Germany, 1986.

11. Levich V.G., Physicochemical Hydrodynamics (Prentice Hall, Englewood Cliffs), 1962.

12. Cini R., Lombardini P.P., Manfredi C. and Cini E., Ripples damping due to monomolecular films, Journal of Colloid and Interface Science, 119, 74-80, 1987.

13. Lombardini P.P., Piazzese F. and Cini R., The Marangoni wave in ripples on an air-water interface covered by a spreading film, Nuovo Cimento, Vol. 5 C, 256-263, 1982.

14. Cini R. and Lombardini P.P.,Experimental evidence of a maximum in the frequency domain of the ratio of ripple attenuation in monolayered water to that in pure water, Journal of Colloid and Interface Science, 81, 125-131, 1981.

15. Hünerfuss H., Alpers W., Lange P.A. and Walter W., Attenuation of wind waves by artificial surface films of different chemical structure, Geophysical Research Letters, Vol. 8, 1184-1186, 1981.

16. Ermakov S.A., Zuikova A.M., Panchenko A.R., Salashin S.G., Talipova T.G. and Titov V.I., Surface film effect on short wind waves, Dynamics of Atmosphere and Oceans, 10, 31-50, 1986.

17. Fiscella B., Gomez F., Pavese P. and Trivero P., Ocean surface films measured by interferential microwave probe, Nuovo Cimento, Vo1.8 C, 375-383, 1995.

18. Holliday D., St-cyr G. and Woods N.E., A radar ocean imaging model for small to moderate incidence angles., Int. Journal of Remote Sensing, Vol. 7, 1809, 1986.

19. Fiscella B., Lombardini P.P., Trivero P., Pavese P. and Cini R., Measurements of the damping effect of a spreading film on wind-excited sea ripples using a two-frequency radar, Nuovo Cimento, Vol. 8 C, 175-183, 1985.

20. Alpers W. and Hünerfuss H., Radar signatures of oil films floating on the sea surface and the Marangoni effect, Journal of Geophysical Research, 93, 3642-3648, 1988.

21. Alpers W., Whismann V., Theis R., Hünerfuss H., Bartsch N., Moreira J. and Lyden J.D., The damping of ocean surface waves by monomolecular sea slicks measured by airborne multi-frequency radars during the SAXON-FPN experiment, International Geoscience and Remote Sensing Symposium, 1987-1990, Helsinki, Finland, 1991. 22. Zecchetto S. and Trivero P., Experimental ocean active microwave remote sensing, Satellite Remote Sensing of the Oceanic Environment, edited by I.S.F. Jones, Y. Sugimori and R.W. Stewart (Tokyo: Seibutsu Kenkyusha Co Ltd), pp. 115-122, 1993.

23. Laur H., Derivation of the Backscattering coefficient σo in ERS-1.SAR PRI Products, ESA doc No: ES-TN-RS-PM-HL09, European Space Agency, France, 1992.

24. Rosenthal W., Lehner S., Hortsmann J., and Koch W., Wind measurements using ERS-1 SAR, Second ERS Application Workshop, 6 – 8 December 1995 (London: ESA), pp. 6-8, 1996.