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Oil Spill Detection in Northern European Waters

Oil Spill Detection in Northern European Waters
Oil Spill Detection in Northern European Waters

Oil Spill Detection in Northern European Waters: Approaches and Algorithms

Anne H.S. Solberg 1, 2 and Camilla Brekke 1, 3

1 Department of Informatics, University of Oslo, Oslo, Norway

2

also at Norwegian Computing Center, Oslo, Norway 3 also at Norwegian Defence Research Establishment, Kjeller, Norway Abstract. The combined use of satellite-based Synthetic Aperture Radar (SAR) images and aircraft surveillance flights is a cost-effective way to monitor deliberate oil spills in large ocean areas and catch the polluters. SAR images enable covering large areas, but aircraft observations are needed to prosecute the polluter, and in certain cases to verify the oil spill. We discuss the limitations of satellite imaging of oil spills compared to aircraft monitoring. Automatic detection of oil spills has proven to be an interesting complement to manual detection. We present an overview of algorithms for automatic detection, and discuss their potential compared to manual inspection as part of an operational oil spill detection framework. Experimental results show that automatic algorithms can perform compa-oil spills, false alarm ratio, and they can also speed up the image analysis process compared to fully manual services.

1. Introduction

Marine pollution arising from illegal oily discharges from ships represents a serious threat to the marine environment. Oil pollution caused by large accidents like the Prestige event in 2002 capture many headlines, but the majority of the oil pollution cases are caused by operational discharges from tankers. Observed oil spills commonly appear in connection with off-shore installations and correlate well with major shipping routes. A combi-nation of aircraft and satellite sensors are currently used to monitor large ocean areas to detect oil spills and catch the polluter. The inclusion of sat-spill surveillance and to cover larger areas.

ellite surveillance allows the user to better target the aircrafts used for oil rable to manual detection, both in terms of accuracy in detecting verified 359

V. Barale, M. Gade (eds.), Remote Sensing of the European Seas.? Springer Science+Business Media B.V. 2008

360 A.H.S. Solberg and C. Brekke

2. Remote sensing sensors for oil spill detection

For routine monitoring of illegal oil discharges from ships and offshore installations both aircraft sensors and satellite sensors can be used. Satellite-based Synthetic Aperture Radar (SAR) images can be used to screen large ocean areas, while aircrafts are more suitable to be brought into action to identify the polluter, the extent, and the type of spill.

2.1 Aircraft sensors

Most surveillance aircrafts used for oil pollution monitoring in Europe are equipped with a combination of sensors: Side-Looking Airborne Radar (SLAR), infrared/ultraviolet (IR/UV), Laser Fluoro-Sensor (LFS), Micro-wave Radiometer (MWR). For an overview of aircraft sensors for oil spill detection, see (Goodman 1994; Trieschmann et al. 2003). SLAR is the main sensor for long-range detection of oil pollution on the sea surface. The SLAR is used to locate possible spill locations. Then the spill is ins-pected more closely using additional sensors and/or visual inspection. The sensor configuration used on board surveillance aircrafts varies from coun-try to country. An example is the German aerial surveillance, which lo-cates the oil spills by SLAR, IR/UV scanning is used to quantify the extent of the film, a MWR is used to quantify the thickness, and a LFS is used for oil type classification. The SLAR, IR, and LFS can operate at night. A number of different aircraft types are used for oil spill aerial surveillance. They differ in terms of endurance, cruising speed and SLAR sensor equip-ment resulting in different SLAR area coverage during one flight hour. One hour of airborne remote sensing over the sea at a speed of 335 km/h covers an area of 13400 km2 (Tufte et al. 2005).

2.2 Satellite sensors

SAR is the main spaceborne remote sensing instrument for oil spill imag-ing, with all-weather and all-day operation capabilities, although it is not capable of oil spill thickness estimation and oil type recognition. The main limitation for spaceborne optical sensors is the need for daylight and cloud-free scenes, but they have a potential to discriminate between oil and algal blooms. A more detailed discussion of other satellite sensors for oil spill detection is given in (Brekke and Solberg 2005).

SAR is particularly useful for searching large areas. Usually even small volumes of oil cover large areas and thus the need for very high

Oil Spill Detection in Northern European Waters 361

spatial resolution in SAR images is not crucial. SAR has however some limitations, as a number of natural phenomena can produce similar dark objects in the SAR images (see Section 3).

Currently, RADARSAT-1 and ENVISAT ASAR are the two main SAR sensors used for oil spill detection. The best trade-off between spatial cov-erage and spatial resolution is achieved using RADARSAT-1 ScanSAR and ENVISAT ASAR Wide Swath image modes. Table 1 describes the coverage and resolution of these sensors. The costs of satellite images are much lower than the costs of covering the same area by aircraft. The actual time that the satellite passes over a given location will vary with latitude, but the overflight will be fixed in time.

ASAR products.

RADARSAT-1 ScanSAR Narrow ENVISAT ASAR Wide Swath

Spatial coverage per scene Spatial resolution per scene 300 km × 300 km 50 m × 50 m 400 km × 400 km 150 m × 150 m

The number of available RADARSAT-1 or ENVISAT ASAR images of a given area on a given date depends on the geographic latitude and the observation period. Daily coverage is possible in Northern Europe, and a coverage a couple of times a week is possible for all European waters.

2.3 The advantages and limitations of satellite-based vs. aircraft monitoring are summarized in Table 2. In order to cover the same area as a RADARSAT-1 ScanSAR Narrow scene or an ENVISAT ASAR Wide Swath scene with an aircraft, 6 or 12 flight hours, respectively, are needed. A limitation with the satellite monitoring is that the images are taken at fixed times of the day. The fate and persistence of oil in seawater are controlled by processes that vary considerably in space and time. The amount of oil spilled, its initial physical and chemical characteristics, and the prevailing climatic and sea conditions have great impact on the lifetime of an oil spill. A reasonable as-sumption might be that most illegal oil discharges are bilge oil, i.e. a mixture of several kinds of oils (fuel oil, hydraulic oil, etc.). For instance, in the Fin-nish surveillance area it is estimated that 1–5 percent of the detected slicks persists only some hours (Tufte et al. 2005). To cover oil spills occurring at Satellite vs. aircraft – advantages/limitations

Table 1. Coverage and resolution for selected RADARSAT-1 and ENVISAT are thicker slicks that persist several days, and 95–99% are bilge oil that

362 A.H.S. Solberg and C. Brekke

all times of the day, aerial surveillance can be used to supplement the fixed coverage times of the SAR satellites.

Table 2. Advantages and drawbacks with SAR satellite and aerial surveillance. (Adapted from Tufte et al. 2005).

Satellite SAR Aerial surveillance Advantages Advantages lance.

aircraft operational efficiency.

Flexible monitoring. Can be deployed at short notice. Can identify additional oil parameters. Limitations Limitations False targets can occur in analysis.

Fixed monitoring schedule.

Limited to certain wind conditions. High cost. Smaller spatial coverage. Limited to certain weather conditions.

3. SAR imaging of oil spills

Oil spills dampen the Bragg waves (wavelength of a few cm) on the ocean surface and reduce the radar backscatter coefficient. This results in dark regions or dark spots in a satellite SAR image. A part of the oil spill detec-tion problem is to distinguish oil spills from other natural phenomena that dampen the short waves and create dark patches on the surface. Natural dark patches are termed oil spills look-alikes. Oil spills include all oil re-lated surface films caused by oil spills from oilrigs, leaking pipelines, pass-ing vessels as well as bottom seepages, while look-alikes include natural films/slicks, grease ice, threshold wind speed areas, wind sheltering by land, rain cells, shear zones, internal waves etc.

A service for oil spill detection based on SAR images must contain an oil spill detection step where the SAR images are analyzed, and dark re-gions that might be oil spills are identified. In this process, the factors that can be used to discriminate between an oil spill and a look-alike are impor-and provide larger damping to the surrounding sea than natural films (Hovland et al. 1994). A newly released oil spill will have reasonably sharp borders to the surrounding sea. As the weathering effects the spill, the borders can become more fuzzy. The shape will be altered by wind and current. Depending on the source of the outlet, certain shapes of the oil spills can be expected. Oil spills from moving ships are thin, linear or

tant. Due to higher viscosity, oil spills tend to remain more concentrated High accuracy of oil spill detection. Can identify polluter. Large and well-defined spatial coverage.

Less expensive than airborne surveil-

Can be used to cue aircraft to improve

Oil Spill Detection in Northern European Waters 363 piecewise slicks, while oil spills from stationary sources can be wide and regular if a significant amount of oil is released in short time.

Other types of pollution can also cause slicks that are visible in the SAR image Algae can also create dark patches in the SAR image very similar to oil spills. This is in particular a problem in the Baltic Sea, where a certain algae type that dampens the Bragg waves is common during the summer season. If additional information from e.g. ocean color sensors is available, it can be used to identify algae.

The wind speed is also important for imaging of oil spills. With very low wind, no backscatter from the sea surface will be seen. Look-alikes are very frequently observed in low to moderate wind conditions (approxi-mately 3 to 7 m/s). As the wind speed increases, the expected number of look-alikes will be lower. For oil spills, the contrast between the spill and the surrounding sea will decrease with higher wind speeds. At high wind speeds (>10 m/s) only larger slicks with thicker oil will be visible. The up-per limit for observing oil in the SAR image is not known exactly. In an operational oil spill detection service at Kongsberg Satellite Services (KSAT) in Troms?, Norway, an upper limit of 15 m/s is used.

4. SAR oil spill detection: manual vs. automatic

Oil spills in a SAR image can be identified by manual inspection, or the image can first be screened by an automatic algorithm for oil spill detec-tion, followed by manual inspection of the suspect alarms only. Manual interpretation of e.g. a 400 km × 400 km ENVISAT ASAR image can be a complex and time consuming task because the image is so large that the operator can only view a small part of the scene at a time to be able to de-tect thin oil spills. Recent benchmarks (Indregard et al. 2004; Solberg et al. 2006) comparing automatic algorithms to manual detection shows that an automatic algorithm can be a valuable tool when a large number of images are to be inspected.

4.1 Manual oil spill detection

A well-established operational service for oil spill detection is run at KSAT. Trained operators detect oil spills by inspecting the SAR images. In addition to the image, they can use external information about wind speed and direc-tion, oil rig/pipeline location, national territory borders and coast lines. After a possible oil spill has been detected, it is assigned confidence level low, medium or high based on a certain set of rules (Indregard et al. 2004).

364 A.H.S. Solberg and C. Brekke

4.2 Automatic approaches

A literature review of automatic techniques for oil spill detection in SAR images can be found in Brekke and Solberg (2005 ). In this section we give a short update on the state-of-the-art in this field.

Several of the published papers on oil spill algorithms for SAR images (e.g. Fiscella et al. 2000; Del Frate et al. 2000; Solberg et al. 1999, 2006) describe a methodology consisting of dark spot detection followed by fea-ture extraction and a classification step (see Figure 1).

Fig. 1. A framework for oil spill detection algorithms.

Segmentation techniques

As oil spills are characterized by low backscattering levels, the use of thresholding for dark spot segmentation is commonly applied (see e.g. Keramitsoglou et al. 2006; Solberg et al. 1999; Brekke and Solberg 2005). As SAR images tend to become darker with increasing range and as local variations in the wind level and other meteorological and oceanic condi-tions occur, thresholding algorithms where the threshold is set adaptively based on local statistical estimates should be preferred.

Dark spot feature extraction

Discrimination between oil spills and look-alikes are often based on a number of features computed for each suspicious dark spot on the sea sur-face (see e.g. Topouzelis et al. 2002; Solberg et al. 1999; Brekke and Sol-berg 2006; Del Frate et al. 2000; Fiscella et al. 2000).

Good features are very important for the success of the following classi-fication step. Most of the features applied in the literature are covered by the following types:

? the geometry and shape of the dark spot;

The location of detected oil spills and their confidence level is then immediately

sent to the surveillance aircrafts.

Oil Spill Detection in Northern European Waters 365 ?the physical characteristics of the backscatter level of the dark spot and its surroundings;

?the dark spot contextual features;

?the texture features of both the dark spot and the surroundings.

Slick classification

The purpose of the classification step is to distinguish oil spills from look-alikes. How difficult the classification task is depends on the variability in the feature values for objects in the oil spill class relative to the difference between feature values for objects in the look-alike class. Effective methods for developing classifiers involve learning from example patterns (training). In statistical approaches, the classification decision is based on the probabil-ity and the cost of a certain decision (e.g. Solberg et al. 1999; Brekke and Solberg 2007; Brekke et al. 2007).

Various classifiers have been applied to the oil spill detection problem (Fiscella et al. 2000; Nirchio et al. 2005; Del Frate et al. 2000; Keramitso-glou et al. 2006). All detection algorithms suffer from false alarms, and dark spots classified as oil spills may be confused with look-alikes (e.g. natural film and low wind areas). Applying external data to improve classification and assess the slick nature has been suggested. Girard-Ardhuin et al. (2005) combines characteristics of the detected dark spots from the SAR images and meteorological and oceanic data through a multi-sensor approach (in-cluding information about surface wind measurements, sea-surface tempera-ture, atmospheric fronts and clouds and chlorophyll).

5. A benchmark study of oil spill detection approaches As part of the EC project Oceanides, a benchmark study comparing oil spill recognition approaches was performed. Manual oil spill detection based on SAR images was compared to semi-automatic and automatic ap-proaches. A joint satellite-airborne campaign was performed during 2003. The campaign covered the Finnish and German sectors of the Baltic Sea, in addition to the German sector of the North Sea. The campaign was or-ganized in such a way that a trained operator at KSAT (KSAT1) analyzed the SAR images, and reported possible oil spills to the Finnish and German pollution control authorities. They would check the positions and verify the slicks, and report additional slicks found by the aircraft. This was done for both ENVISAT and RADARSAT-1 images.

366 A.H.S. Solberg and C. Brekke

Center (NR) (Solberg et al. 2006) was used to analyze all images without knowing aircraft detections or KSAT results. Figure 2 shows examples of correctly classified oil spills, false alarms, and slicks detected only by aircraft.

Examples of false alarms. The slick in the image on the left was verified as algae, while the clearly suspect dark spot in the right image could not be found when the aircraft inspected

the position (possibly resolved).

For benchmark comparisons, KSAT let another operator (KSAT2) inspect the same SAR images without knowing the aircraft detections or the automatic oil spill detection approach developed at Norwegian Computing

result of the previous inspection to study the inter-operator variance. The ESA/KSAT/NR.

? Fig. 2. ENVISAT images with examples of oil spills and false alarms.

Oil Spill Detection in Northern European Waters 367 The benchmark data set contained 27 ENVISAT images and 32 RADARSAT-1 images. The real-time inspection of the ENVISAT images at KSAT (KSAT1) detected 11 oil spills that were verified as oil spills by the aircraft. The repeated inspection by another operator, KSAT2, detected 8 of these verified slicks, the automatic algorithm (NR) also detected 8 of the verified slicks. For RADARSAT-1 data, there was 18 verified oil spills, KSAT2 found 15 of these, while the NR algorithm found 14.

This demonstrates that the inter-operator variance was significant. KSAT has later taken measures to reduce this variability by increasing op-erator training and harmonising the interpretation process. The perform-ance of the NR algorithm is also almost comparable to KSAT2, so it can be a valuable alternative or supplement to manual inspection.

It is also of interest to study additional slicks reported by the aircraft, but not detected from satellite image analysis. In general, these slicks involved a small amount of oil. For some of them, the time of aircraft pass was sev-eral hours after the satellite image acquisition, so the release could be new. For other cases, the satellite image was taken several hours after the air-craft pass, and a small amount of oil could very well be resolved during this time period.

The number of false alarms, the number of slicks reported by satellite, but verified as not oil by the aircraft, was also studied. All false alarms are discussed in detail in (Indregard et al. 2004). Some linear slicks with good contrast were detected in the satellite image but verified as algae.

The confidence levels that are associated with reported possible oil spills from KSAT can be used by the surveillance aircraft crew to prioritize which oil spill positions they will inspect first. Slicks that were assigned confidence level High had very low false alarm ratio, slicks that were assigned confi-dence Medium had reasonably low false alarm ratio, while slicks that were assigned confidence Low had relatively high false alarm ratio (again the de-tails can be found in (Indregard et al. 2004). However, the confidence as-signed by two different operators at KSAT varied. The automatic algorithm can also be used to compute confidence levels using a set of rules that simu-late the rules used by the operators at KSAT (see Solberg 2005 for how this is done). By comparing the confidence levels assigned by two KSAT opera-tors and NR’s automatic algorithm on a set of 22 selected oil spills, it was found that KSAT1 and KSAT2 assigned the same confidence for 7 out of 22 slicks, while the algorithm and KSAT1 agreed for 13 of 22 slicks, and had a confidence level difference of one (indicating e.g. High vs. Medium) for six additional slicks. What this indicates is that there is still some subjectivity involved in confidence assignment, and using an algorithm to get a “second opinion” to use in confidence assignment might be valuable.

368 A.H.S. Solberg and C. Brekke

The processing time for manual inspection at KSAT varied between 3 and 25 minutes for RADARSAT-1 images, with an average of 9 minutes. The automatic algorithm had an average processing time of 3 minutes. For ENVISAT images, the average time for manual inspection at KSAT was 10 minutes, while the automatic algorithm had an average processing time of 1.45 minutes.

6. Discussion and conclusions

The combined use of satellite-based SAR images and aircraft surveillance flights is a cost-effective way to monitor large areas and catch the pollut-ers. The coverage in terms of the number of weekly satellite passes of European waters is very good in Northern Europe and decent in the Medi-terranean.

Oil spill detections from aircrafts and satellite images were compared in a benchmark study. In general, there were good agreement between aircraft detections and satellite-based detections when the time offset between the image acquisitions was low.

Some information about oil spill statistics and hot spots exist, see e.g. (Bauna and Clayton 2004; Tufte et al. 2005). Hot spots coincide well with major shipping routes, pipelines, oil rigs etc. Tufte et al. (2005) discuss sampling requirements as guidelines for operational oil spill monitoring in European waters. They also summarize current monitoring efforts for many countries in Northern Europe, and their experience in using a com-bination of aerial- and satellite monitoring. The best approach for a spe-cific national authority depends on the size and shape of the area to be monitored, and other resources available. International cooperation with neighboring countries on planning satellite acquisitions and sharing costs is important.

Many different techniques are proposed for automatic detection of oil spills. They often consist of three main parts: segmentation, feature extrac-tion and classification. However, there is still a challenge in reducing the number of false alarms, and automatically assigned confidence levels can be helpful in prioritizing the alarms. Interoperator variance in manual de-tection should be reduced by better training, or introducing the algorithm as a second information source. This applies to both detection and confi-dence assignment.

Presently, one of the biggest challenges for operational oil spill de-tection services is obtaining sustainability in terms of data availability. SENTINEL-1 is a planned two-satellite system (C-band) to be operated as

Oil Spill Detection in Northern European Waters 369 a constellation for maximized coverage/repeat cycle. The first satellite will be launched in 2010 and the second some 12–15 months later (Attema 2005 ). RADARSAT-2 is a Canadian SAR satellite planned to be launc-hed in the summer of 2007. The SAR instrument will be C-band like RADARSAT-1, but there will be more flexibility in the selection of polarizations.

Acknowledgements

The authors would like to thank the Oceanides project, in particular Marte Indregard, Peter Clayton and Lars Tufte for contributions to the benchmark study on oil spill approaches.

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