IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 3, JULY 2010 545

Ship Detection Using TerraSAR-X Imagesin the Campos Basin (Brazil)Rafael L. Paes, Joao A. Lorenzzetti, and Douglas F. M. Gherardi

Abstract—The very large extent of the Brazilian coast(∼8000 km) and the growing maritime vessel traffic demand thatresearch be made on ancillary methods to monitor and controlship’s traffic in national waters. An important tool for this purposeis the use of orbital synthetic aperture radar (SAR) imagery, par-ticularly due to its ability to work day and night and to suffer al-most no interference of cloud coverage. In this letter, we investigatesome ship detection concepts, as applied to TerraSAR-X (TSX)ScanSAR images (16-m resolution), in VV and HH polariza-tion. Ocean clutter statistical parameters are estimated, and theKolmogorov–Smirnov test is used to verify the goodness of fitfor the K-distribution to TSX images. A constant false alarmrate (CFAR) target detection algorithm is developed, and its per-formance is verified. Incidence angle, CFAR’s window size, andprobability of false alarm influence are further analyzed.

Index Terms—Constant false alarm rate (CFAR), estimation,K-distribution, ship detection, TerraSAR-X (TSX).

I. INTRODUCTION

THE CAMPOS Basin covers the continental shelf, slope,and deep water at the southeast Brazilian coast. This is a

region of extensive offshore oil exploration and production. Asso, it has many oil platforms and is subjected to an intensive andgrowing maritime traffic. Risks of collisions or oil spill eventsare a constant concern. Oil platforms and ships can be moni-tored or even managed with the support of a surveillance systembased on satellite synthetic aperture radar (SAR) images. Theefficiency of this methodology has been tested and proved in thescientific literature, as previously reported in [1] and [4]. Recentterrorism acts and maritime piracy around the world highlightthe need for this kind of application too. In this letter, we presentthe results of a constant false alarm rate (CFAR) ship detectionalgorithm that is applied to X band SAR images obtained fromthe German TerraSAR-X (TSX) satellite.

II. DATA AND METHODS

The TerraSAR satellite carries an X-band multimode SARflying on a sun-synchronous dusk–dawn near-polar orbit plat-form at an altitude of 514 km. Three HH and two VV polar-ization modes, 16-bit amplitude TSX images (Table I), have

Manuscript received July 31, 2009; revised December 2, 2009. Date ofpublication March 4, 2010; date of current version April 29, 2010.

R. L. Paes is with the IEAv Geointelligence Division, Institute of AdvancedStudies, São Jose dos Campos 12228-970, Brazil (e-mail: rlpaes@ieav.cta.br).

J. A. Lorenzzetti and D. F. M. Gherardi are with the INPE Remote SensingDivision, National Institute for Space Research, São José dos Campos 12227-010, Brazil (e-mail: loren@dsr.inpe.br; douglas@dsr.inpe.br).

Color versions of one or more of the figures in this letter are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/LGRS.2010.2041322

TABLE IGENERAL CHARACTERISTICS OF THE ANALYZED TSX IMAGES

been used for ship/target detection within the Campos Basin.This area includes all offshore oil platforms and the vicinityof the Rio de Janeiro harbor. To increase the geographicalcoverage, all images were acquired in ScanSAR mode. Imageswere radiometrically corrected, georeferenced, and multilookpreprocessed. For ground truth, we used ship location andcharacterization data from the Automatic Information System(AIS), and the position of offshore oil platforms providedby the Brazilian oil company Petrobras. Meteorological andsurface wind conditions, as coincident as possible with the TSXimages, were obtained from GOES-10 and Quickscat satellites.

To minimize computational time, full-size 17 000 ×20 000 pixels amplitude images were fractioned into smallerimage subsets of about 5000 × 5000 pixels each. Each imagewas converted to intensity values, from which vertical(azimuth) and horizontal (range) spatial autocorrelations amongpixels were calculated along the x and y directions. TheKolmogorov–Smirnov (KS) test was used to verify thegoodness-of-fit of the K-distribution to the ocean clutter ofTSX images. The K-distribution parameters are sample mean;equivalent number of looks (ENL), which can be obtainedfrom [5]

ENL = Mean2

V ariance(1)

and K-distribution order parameter (v), solved numericallyfrom [6]

ln v − ψ(0)(v) +v

ENL

⎛⎜⎜⎜⎝

1M

M∑i=1

x2i(

1M

M∑i=1

xi

)2 − 1

⎞⎟⎟⎟⎠

= ln

(1M

M∑i=1

xi

)− 1

M

M∑i=1

lnxi +1

2ENL(2)

1545-598X/$26.00 © 2010 IEEE

546 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 3, JULY 2010

where M is the number of the sample elements, ψ(0) is adigamma function, and xi is the intensity value of each elementsample.

The ship detection scheme used here is based on a localprocess algorithm called CFAR that finds image samples withbrightness values that are statistically higher than the surround-ing ocean clutter [3], [7], [8]. The CFAR algorithm prescreensthe entire working image and calculates an adequate detectionthreshold based on previously adopted probability of falsealarm (PFA) and the estimated distribution parameters. Thisis a threshold wherein, above it, pixel values are consideredtargets and, below it, ocean clutter. To verify CFAR detectionperformance, we cross checked the detected pixel clusters withthe ground truth from the AIS data [8]. A figure-of-merit (FoM)given by (3) has been employed as a detection performancemeasurement [2]

FoM =Ntd

Nfa + Ngt(3)

where Ntd is the number of total true detections, Nfa is thenumber of false alarms, and Ngt is the number of ground truthtargets.

As proposed by [8], target, buffer, and background windowsize configurations were, respectively, 5 × 5, 7 × 7, and 15 ×15 pixels. Hereinafter, we refer to those window configurationsas 5_7_15. To calculate the threshold value (t) that separatestargets from nontargets, (4) is solved numerically [4]. For this,the user has to define a PFA value, while the other parameters(K-distribution order parameter and sample mean) are esti-mated from image samples interactively. The CFAR algorithmcompares the mean value of the target window to the selectedthreshold: if it is equal or higher than the threshold, then it isaccepted as target; otherwise, it is rejected [4]

PFA(K) =2

Γ(νB)

(νBt

μB

) νB2

KνB

[2√

νBt

μB

](4)

where μB is the mean value from ocean clutter backgroundwindow, t is the target window’s central value that will bechecked as a possible threshold corresponding to a predefinedPFA, νB is the K-distribution order parameter, and KνB

is aBessel function of a second kind.

III. RESULTS AND DISCUSSION

A. Parameter Estimation and SpatialAutocorrelation Checking

High-resolution TSX images require a careful choice ofwindow size for parameter estimation since strong brightnessvariations will affect the correct estimation. Image inspectionsuggested that regions corresponding to the long ocean wavetroughs are relatively homogeneous (with little brightness vari-ation), being appropriate sites for parameter estimation. TheK-distribution order parameter is very sensitive to brightnessvariations which have direct influence on successful results.

Before going through the parameter estimation procedure,it is necessary to calculate the spatial decorrelation distanceof image samples. A maximum spatial autocorrelation of 0.2

Fig. 1. July 12, 2008, image showing the presence of (red box, top image)cumulus–nimbus clouds that are important causes of false alarms. (Bottomleft) Zoom image of the effect of clouds on the (red box) brightness contrastresponsible for the production of (bottom right) false alarms.

between two pixels was considered as reasonably low, and itgenerated consistent results. For the analyzed TSX images,such decorrelation was observed at 3 pixels in azimuth and2 pixels in range. Autocorrelation values obtained from near,mid, and far range samples produced very similar results forall estimations. Starting with a 30 × 20 pixel sample, after a3 × 2 image resampling for spatial decorrelation, image sam-ples formed a 121-element vector. This vector size gives arelatively small estimator variance for multilook images suchas the TSX [6]. To run the KS test, a synthetic K-distributedsample was generated [7], and a comparison is made betweenimage and synthetic sample cumulative distributions.

B. Incidence Angle, Window Setup, and the Influence of PFA

At low wind intensity (< 5 ms−1) and with no rain cells,false alarms were more frequent at far range, and missingtargets tended to occur at near range. This is likely to occurbecause the algorithm compares the mean values inside thewindows. In the far range, where the background clutter ap-proaches the noise floor level, some isolated speckle pixels cansometimes be incorrectly considered as target. In the near range,the algorithm may not eventually distinguish the tonal variationbetween the two windows because the background pixel valuesare high compared to the target window. When wind and waveheight increase, false alarms can appear even at the near range.

Meteorological conditions and incidence angle influencedthe choice of the threshold value in such a way that, when thebackground backscattering is high, the threshold is also higherthan usual. Fig. 1 shows an example of a cumulus–nimbus cloud(probably with strong rain rate) as source of false alarm. Thestrong backscatters that occupy a sizeable area can be easily

PAES et al.: SHIP DETECTION USING TERRASAR-X IMAGES 547

Fig. 2. Detection scheme where the shifting window set computes the localstatistics and applies a decision rule based on the PFA.

Fig. 3. Smallest detected target on an image taken on August 9, 2008.AIS data provided by AISLive.com service. Estimated wind velocities varyfrom 1 to 5 kt.

(and incorrectly) classified as target. Attempts to correct theproblem by increasing the threshold and/or varying the windowsizes were not successful.

Particularly at near range and at higher sea state conditions,it was not possible to select a PFA that could completelyavoid false targets. Sometimes, changing the sliding windowconfiguration can improve the results. After some empiricalverification, we observed that the window configuration 5_7_15was the most appropriate for this image resolution. Therefore,a good compromise for regular situations is a window set of5_7_15 with a PFA of 1%.

C. Algorithm Operation and Detection Performance

The algorithm sliding windows move pixel by pixel, compar-ing the background with the target window samples, as shownin Fig. 2. At each step, if the central pixel has a value smallerthan the calculated threshold, then a “zero” value is insertedat its original position. If it is accepted (a pixel value equal orhigher than the threshold), the original pixel value is kept in theimage. The processed image will have a black background, andthe bright pixels will correspond to true targets or false alarms.

If two targets are very close to each other, about one totwo pixels apart, it is possible that the algorithm groups themtogether as one single target or split them apart, depending onthe targets’ shapes. If two separate targets have a thin outline,they can be separated; otherwise, they tend to be grouped.

Fig. 4. Example of detected targets for the July 24, 2008, image, confirmedby Platform position data provided by Petrobras. (Top) Original and (bottom)processed images. Estimated wind velocities vary from 5 to 10 kt.

Fig. 5. Example of detected targets on an image taken on July 12, 2008,confirmed by a patrol aircraft. (Top) Original and (bottom) processed image.Estimated wind velocities vary from 15 to 20 kt. Ship picture source: VesselTracker Web site www.vesseltracker.com).

As our ground truth sources for ships were basically theAIS data, detection was focused on larger vessels. Our smallestdetected target that was checked against the AIS was found onthe August 9, 2008, image for a 16-m-length ship (Fig. 3). Dueto a range limitation of AIS, true targets that are not present inthe AIS data can sometimes be detected [Fig. 6(a)]. Dependingon the shape and pixel intensities, these cases were empiricallyanalyzed, as discussed below. Figs. 4 and 5 show examples ofother relevant detection results.

The detection performance for TSX imagery is shown inTable II. The lowest FoM is observed for the July 24 image.

548 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 7, NO. 3, JULY 2010

TABLE IIDETECTION PERFORMANCE

Fig. 6. Differences between true target and false alarm. (a) July 12, 2008,image: ship with no AIS ground truth but with shape and intensity similar tothose previously identified. (b) July 24, 2008, image: example of false alarm.Shape and intensity of pixel cluster are not sufficiently high to be considered atrue target.

This was caused by many bright round clusters of pixels thatwere not accepted as targets [Fig. 6(b)], which reduced thedetector performance.

D. AIS Data and Maritime Patrol Flights: GroundTruth Reliability

As it is normally the case for ship detection studies, our majorproblem was to obtain an appropriate ground truth data setthat covered such very large oceanic area. Vessels using AISequipment and operating on VHF are subject to Earth’s cur-vature and meteorological influence on RF wave propagation.Thus, vessels imaged very far from the coast normally are notpresent in the AIS database. For example, in the July 12, 2008,image, the analyzed area is about 200 km far from the coast.Oil platforms that are far from the coast were easily detectedand verified by their positions provided by the Brazilian oilcompany.

In an attempt to minimize the AIS range problem, maritimepatrol aircrafts from Brazilian Air Force were employed tocover blank areas. Although an additional area has been covered

by an aircraft, sometimes air traffic restrictions made it difficultto obtain the ships positions at the same time of satellite pass.

In these cases, bright pixel clusters considered as targetsby the algorithm, but without ground truth information, hadtheir shapes and intensities compared to those that could beverified. Only targets that looked like the real targets (shape andintensity) were accepted, as shown in Fig. 6(a).

IV. CONCLUSION

As far as ship detection is concerned, our results have indi-cated that TSX ScanSAR mode is a good compromise betweena reasonable area coverage (100 × 150 km), spatial resolution(16 m), and detection capability (well-defined target outlines).Polarization influence was not analyzed due to the small num-ber of images available on our data set. Window set of 5_7_15and 1% of PFA showed the best results and is recommendedhere as a default configuration. The K-distribution fitted tothe image samples and parameter estimation led to successfuldetections. The CFAR algorithm applied to the TSX imageryshowed an average detection performance of 82% as measuredby the FoM metric, without any postprocessing technique. Re-sults have also indicated that the presence of heavy and possiblyrainy cumulus–nimbus clouds and high sea state can reduce theoverall accuracy of the CFAR by creating false alarms.

ACKNOWLEDGMENT

The authors would like to thank Infoterra for generouslyproviding the TSX images, Cenpes/Petrobras for providing oilplatform positions used as ground truth, AISLive.com and theBrazilian Navy for kindly providing the AIS data, and theBrazilian Air Force Maritime Patrol Squadrons for providingthe ground truth by locating vessels during patrolling missions.

REFERENCES

[1] D. J. Crisp, “The state-of-the-art in ship detection in synthetic apertureradar imagery,” Dept. Defence, DSTO, Australian Government, Edinburgh,Australia, 2004.

[2] S. B. Foulkes and D. M. Booth, “Ship detection in ERS and radarsatimagery using a self-organising Kohonen neural network,” in Proc. Conf.Ship Detection Coastal Waters, Digby, NS, Canada, 2000.

[3] M. Liao, C. Wang, Y. Wang, and L. Jiang, “Using SAR images to detectships from sea clutter,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2,pp. 194–198, Apr. 2008.

[4] R. E. Moutray and A. M. Ponsford, “Integrated maritime surveillance:Protecting national sovereignty,” in Proc. Radar Conf., Sep. 3–5, 2003,pp. 385–388.

[5] C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images.Boston, MA: Artech House, 1998.

[6] N. J. Redding, “Estimating the parameters of the K distribution in intensitydomain,” Surveillance Syst. Div., Electron. Surveillance Res. Lab., DSTO,Edinburgh, Australia, Jul. 1999.

[7] M. T. Rey, A. Drosopoulos, and D. Petrovic, “A search procedure for shipsin RADARSAT imagery,” DREO, Ottawa, ON, Canada, Nov. 1996.

[8] P. W. Vachon, J. Campbell, C. Bjerklund, F. Dobson, and M. Rey, “Shipdetection by the RADARSAT SAR: Validation of detection model predic-tions,” Can. J. Remote Sens., vol. 23, no. 1, pp. 48–59, 1997.

[9] C. Wackerman, P. Clemente-Colon, W. Pichel, K. Friedman, and X. Li,“Automatic detection of ships using RADARSAT-1 SAR imagery,” in Proc.Conf. Ship Detection Coastal Waters, Digby, NS, Canada, 2000.