removal of azimuth ambiguities and detection of a ship: using...
TRANSCRIPT
International Journal of Remote SensingVol. 33, No. 10, 20 May 2012, 3197–3210
Removal of azimuth ambiguities and detection of a ship: usingpolarimetric airborne C-band SAR images
CHANGCHENG WANG†‡, YONG WANG†§ and MINGSHENG LIAO∗††State Key Laboratory of Information Engineering in Surveying, Mapping and Remote
Sensing, Wuhan University, Wuhan, Hubei 430079, PR China‡School of Info-Physics and Geomatics Engineering, Central South University, Changsha,
Hunan 410083, PR China§Department of Geography, East Carolina University, Greenville, NC 27858, USA
(Received 16 July 2010; in final form 17 August 2011)
Synthetic aperture radar (SAR) imagery from the sea can contain ships and theirambiguities. The ambiguities are visually identifiable due to their high intensitiesin the low radar backscatter background of sea environments and can be mistakenas ships, resulting in false alarms in ship detection. Analysing polarimetric char-acteristics of ships and ambiguities, we found that (a) backscattering from a shipconsisted of a mixture of single-bounced, double-bounced and depolarized or dif-fused scattering types due to its complex physical structure; (b) that only a strongsingle- or double-bounce scatterer produced ambiguities in azimuth that looklike relatively strong double- or single-bounce scatterers, respectively; and (c) thateigenvalues corresponding to the single- or double-bounce scattering mechanismsof the ambiguities were high but the eigenvalue corresponding to the depolarizedscattering mechanisms of the ambiguities was low. With these findings, we pro-posed a ship detection method that applies the eigenvalue to differentiate the shiptarget and azimuth ambiguities. One set of C-band JPL AIRSAR (Jet PropulsionLaboratory Airborne Synthetic Aperture Radar) polarimetric data from the seahave been chosen to evaluate the method that can effectively delineate ships fromtheir azimuth ambiguities.
1. Introduction
Synthetic aperture radar (SAR) images have been widely used in maritime applica-tions such as ship detection, traffic monitoring and immigration control (Vachon et al.2000, Wackerman et al. 2001). For a SAR image of single polarization, a ship can beseparated from sea clutter with an appropriate choice of threshold of radar cross sec-tion (RCS), because the RCS of the ship is stronger than the surrounding sea clutter.Numerous studies have been conducted to detect ships using the amplitude values ofsingle-polarization SAR images (Eldhuset 1996, Vachon et al. 2000, Wackerman et al.2001, Kuo and Chen 2003, Liao et al. 2008, Wang et al. 2008). With the availability ofpolarimetric SAR (PolSAR) sensors that measure scattering characteristics of a tar-get, researchers also use the data in ship detection. Early work has been focused onthe development of an optimum detection method using the knowledge of known tar-get or clutter scattering parameters. Novak and Burl (1990) proposed a polarimetric
*Corresponding author. Email: [email protected]
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis
http://www.tandf.co.uk/journalshttp://dx.doi.org/10.1080/01431161.2011.633123
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3198 C. Wang et al.
whitening filter (PWF) method that generates a minimum-speckle image for the targetdetection by combining three complex elements (HH, HV and VV) of the polarimet-ric scattering matrix. Sciotti et al. (2001) proposed a number of detection schemesusing PolSAR data. For instance, one scheme is to fuse polarimetric images by usingthe PWF technique and then apply a power ratio (PR) detector to detect the fusedimage. Chen et al. (2009) used a polarimetric cross-entropy (PCE) method. They firstdecomposed coherency matrices from the target and clutter and then used a constantfalse alarm rate (CFAR) detector for the ship detection with the assumption that thePCE follows a generalized exponential distribution. However, these approaches mightlead to questionable outcomes due to the complexity of PolSAR images from the shipand sea. One of the complexities is due to azimuth and range ambiguities of a shiptarget. The ambiguities have an impact on the accuracy of the detection. The azimuthambiguity is caused by the finite sampling of the Doppler spectrum at the intervalsof the pulse repetition frequency (PRF). Since the spectrum repeats at PRF intervals,the signal components outside this frequency interval fold back into the main part ofthe spectrum. The range ambiguity is due to the time overlap of echoes from the pre-ceding and succeeding pulses, which can arrive at the antenna simultaneously with thedesired return (Curlander and McDonough 1991).
For maritime applications, the ambiguities are visible due to their strong intensitiesin a typical low backscattering background from sea and can be mistaken as ships.Many methods have been proposed to suppress the ambiguities in single-polarizationSAR images (Li and Johnson 1983, Li and Daniel 1985, Freeman 1993, Guarnieri2005, Liu and Gierull 2007). However, the procedures are quite complicated. Also,the characteristics of different types of scattering mechanisms of a target and itsambiguities are not available in single-polarization SAR data. Thus, the character-istics cannot be included in the analysis. Furthermore, while analysing the range andazimuth ambiguities, a researcher has learned that the range ambiguity is typicallynot significant because the spread of echo is very small relative to the inter-pulseperiod. However, the azimuth ambiguities from a target of a given physical dimen-sion can be strong and is typically inversely related to the radar wavelength. Thus,the ambiguous energy is strong at a short wavelength, or the azimuth ambiguity isseverest at the C-band among radar images of C-, L- and P-bands. Also, the spatialdisplacement of the azimuth ambiguities is proportional to the square of the wave-length (Curlander and McDonough 1991), that is, the ambiguities are much closerto the targets or within the area of interest of SAR images in a short-wavelengthsystem than in a long-wavelength system (e.g. L-band). Therefore, a new method todetect ships from airborne C-band PolSAR data is proposed on the basis of the anal-ysis of extracted scattering mechanisms of a target and its azimuth ambiguities aswell as sea clutter. The basic idea is that (a) backscattering from a ship consists ofthe mixture of single-bounced, double-bounced and depolarized or diffused scatter-ing types due to its complex physical structure; and (b) that only a strong single-or double-bounce scatterer produces ambiguities in azimuth that look like relativelystrong double- or single-bounce scatterers, respectively. The 180◦ phase shift is thekey because the ambiguities do not exist as scatterers with strong depolarized scat-tering components, whereas a target does (Raney 1998). One set of JPL AIRSAR(Jet Propulsion Laboratory Airborne Synthetic Aperture Radar) images consistingprimarily of corner reflectors will be used to verify the basis of our development inmethodology. Then, three sets of JPL AIRSAR C-band PolSAR data from the sea willbe analysed to demonstrate this ship detection method. Also, the developed method iscompared with the traditional two-parameter CFAR and PWF methods.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3199
2. Azimuth ambiguities of targets
The azimuth ambiguities arise due to the discrete sampling of the Doppler frequencysignal, which is weighted by the two-way azimuth antenna pattern. Doppler frequen-cies higher than the PRF are folded into the central part of the azimuth spectrumso that aliased signals are produced (Curlander and McDonough 1991, Raney 1998).The azimuth bandwidth (for a given azimuth antenna dimension) varies inversely withradar frequency. Thus, the Doppler shift as a function of a pointing error increaseslinearly with frequency. Unlike other forms of noise or speckle in a SAR image, theambiguities are spatially displaced in azimuth (�xAZ) and range (�xRA) as follows:
�xAZ ≈ nfpvfDR
, (1)
�xRA ≈ nλfp
fDR
(fDC + nfp
2
), (2)
where n is the serial number of ambiguities (n = 1, 2, . . . ), fp is the PRF, v is the veloc-ity of a platform and λ is the radar wavelength. f DR and f DC are the Doppler rate ofthe azimuth reference function and the Doppler centroid frequency, respectively (Liand Johnson 1983, Curlander and McDonough 1991). As shown in equations (1) and(2), the displacement increases as the wavelength increases if other parameters are thesame. Therefore, the ambiguities are less severe in SAR images of a long wavelengththan of a short wavelength. Also, as n increases, the displacements of the related ambi-guities away from the target increases, and their intensities decrease greatly due tothe defocusing of the energy in azimuth and range directions. Thus, the chance ofobserving the ambiguities for n ≥ 2 can be small.
For a spaceborne SAR, the azimuth ambiguities appear to be not well focused inthe spatial or image domains or not very strong in intensities because the spacebornesensor is on board a fast moving platform and typically has a large footprint in whicha large variation in range parameters caused by the Earth’s curvature and local topog-raphy exists. Interestingly enough, this could be an advantage in ship detection fromspaceborne SAR images. In contrast, the platform speed for an airborne SAR is low.Its footprint is also small spatially. Thus, the impact due to the curvature and localtopography on inaccurate range parameters could be minimal, but the energy of theambiguities might be more focused. Thus, the intensities of ambiguities are strong(Raney 1998). In extreme cases, the ambiguities can be still quite visible for n ≥ 2when the amplitude of a target is strong as shown later. Therefore, the focus of thisstudy is the detection of ships with the removal of azimuth ambiguities using airborneC-band PolSAR data.
In general, if the parameters of a SAR are known, as expressed in equations (1) and(2), the locations of the azimuth ambiguities can be derived and used for locating andthen for removing the ambiguities. However, if these parameters are not available inreal time, or if what a user has is only the SAR image, an alternative must be sought.Also, if another target is closely located in the position of the ambiguity, it is difficultto differentiate the target from the ambiguity. Therefore, studying and understandingdifferent types of scattering mechanisms of targets and their ambiguities using PolSARdata is necessary. Thus, we can develop a ship detection strategy with the removal ofazimuth ambiguities.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3200 C. Wang et al.
3. Method of ship detection from PolSAR data and the study area
3.1 Scattering mechanism of the azimuth ambiguities of a ship target
JPL AIRSAR is a PolSAR system (Freeman 1993). H (horizontal) or V (vertical)polarized pulses are transmitted alternatively with equal PRFs. Consequently, theintervals between HH/HV and VH/VV responses are 1 per PRF. The VH/VV returnsare registered and re-sampled with the HH/HV returns. Also a phase shift of VH/VVreturns is removed. After the re-sampling of and correction for the nominal phaseshift, the phase difference between the ambiguous HH/HV and the VH/VV returnsis nπ (Freeman 1993). Also, it is shown that azimuth ambiguities with the strongestamplitude and nearest to the main response or target (i.e. n = 1) occur at predictablelocations and with an HH–VV phase difference of 180◦ from the phase difference asso-ciated with the target. Thus, in PolSAR data, only very bright single- or double-bouncescatterers will produce azimuth ambiguities that look like fairly bright double- orsingle-bounce scatterers, respectively (Freeman 1993). The distinguishable scatteringmechanisms of the target and its azimuth ambiguities are the bases for our algorithmand can be analysed with an eigenvalue–eigenvector decomposition method.
Using the eigenvalue–eigenvector decomposition method, we can decompose thecoherency matrix 〈[T]〉 of each pixel of a PolSAR image into the sum of three inde-pendent targets, each represented by a single coherency matrix [Ti] (Cloude and Pottier1997) as follows:
〈[T]〉 =i=3∑i=1
λi [Ti], (3)
where eigenvalues λ1, λ2 and λ3 (λ1 ≥ λ2 ≥ λ3 ≥ 0) represent weights for each [Ti]. Ifonly one eigenvalue is non-zero (λ1 > 0, λ2 = λ3 = 0), then the coherency matrixcorresponds to a pure point target (e.g. a corner reflector). On the other hand, ifλ1 = λ2 = λ3, the coherency matrix is composed of three orthogonal scattering mecha-nisms with equal weights. It represents a totally random mixture of three scatteringmechanisms. Between two extremes, there exist cases where the coherency matrix hasnon-zero and non-equal eigenvalues.
Owing to the complex structure of a ship target, the scattering mechanisms ofa ship are a mixture of single-bounced, double-bounced and depolarized scatteringmechanisms, since only scatterers of very strong single- or double-bounce scatteringcan create ambiguities in azimuth direction (Freeman 1993). Therefore, the eigen-values corresponding to the single- or double-bounce scattering mechanisms of theambiguities are high, but the eigenvalue corresponding to the depolarized scatter-ing mechanisms of the ambiguities is low. Therefore, we can apply the eigenvaluesto differentiating the ship target and azimuth ambiguities.
3.2 Procedures for the removal of azimuth ambiguities and ship detection
Figure 1 shows the ship detection procedures. Major steps are summarized as follows:
• Converting polarimetric data into coherency matrices for individual pixels. De-speckle if needed.
• Decomposing the coherency matrix of each pixel to obtain eigenvalues λ1, λ2
and λ3.• Using eigenvalue λ3 to compute the local homogeneity of the kernel centred on
each pixel under evaluation.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3201
Figure 1. Flowchart of the removal of azimuth ambiguities and ship detection.
The homogeneity parameter within a local kernel with a typical size of 3 × 3 isderived from the grey-level co-occurrence matrix (GLCM) texture (Haralick et al.1973). The number of grey levels is set as 64. The offsets of the row and columnbetween the pixel-of-interest and its neighbour are set as (1, 1). Then we extract thehomogeneity parameter from the GLCM matrix of the kernel centred on each pixelunder test. If a region within the kernel is homogeneous or less than a predefinedthreshold, the centre pixel is an ambiguity.
3.3 Study areas
Four study areas are selected to evaluate the method. The first location is a flat and drylake bed of Goldstone Lake, CA, USA, where JPL scientists set up numerous dihedraland trihedral corner reflectors and other calibration devices to calibrate and validatethe JPL/AIRSAR (Freeman et al. 1990). Multi-wavelength PolSAR data from this siteare used to verify our method. Then, Kojima-wan Bay, Japan, centred near 133.9◦ Eand 34.4◦ N, is the first site to test the removal of azimuth ambiguities and ship detec-tion method. The AIRSAR data were acquired on 4 October 2000, during the PacificRim 2000 Campaign (Uratsuka et al. 2001). To assess the applicability of the algo-rithm, another AIRSAR dataset of C-band covered sea surface near Tokyo Bay, Japan(centred ∼139.8◦ E and 35.4◦ N), was analysed. The data were acquired on 2 October2000 during the same campaign. Finally, to evaluate whether the algorithm can beused in the situation where azimuth ambiguities exits at n = 1 and n = 2, we will anal-yse C-band AIRSAR data near Anacapa Island, one of the channel islands off thecoast of Ventura County, CA, USA. The data were acquired on 16 April 2003.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3202 C. Wang et al.
4. Results and discussions
4.1 Goldstone Lake
JPL researchers deployed numerous passive and active calibration devices on the drylake bed of Goldstone Lake, about 60 km north of Barstow, CA, USA. The bed is leveland of low radar backscatter at C-, L- and P-bands. Multiple flights of the AIRSARwere performed (Freeman et al. 1990, Wang and Davis 1997). Two sets of multi-wavelength PolSAR data, acquired on 23 May 1988 and 26 July 1989, were analysedand similar results were obtained. Thus, only the 1988 outputs were presented. Figure2(a), (b) and (c) shows the Pauli R (|SHH + SVV|2) G (2|SHV|2) B (|SHH – SVV|2) com-posites of C-, L- and P-band data, respectively. The dark feature in the middle is partof the dry lake bed, surrounded with rugged terrain with relatively moderate or strongbackscattering. Azimuth ambiguities from trihedral corner reflectors (with a side-length of 2.4 m) were observed at the C-band, but not observed in the L- or P-band.The ambiguities were indicated by white arrows. With this analysis, we have verifiedthat the ambiguities occur noticeably for radar targets of strong backscattering(i.e. the 2.4 m long trihedral corner reflectors). The ambiguities are also stronger inintensities and more severe at short wavelengths (C-band) than at long wavelengths(L- or P-band). Finally, the ambiguities from L- or P-bands, if they exist, are muchfarther away spatially from the corner reflectors.
The decomposed scattering mechanisms of C-band polarimetric data are shownin figure 3(a) (single), (b) (double) and (c) (diffused), respectively. Bright signatures
(a) (b) (c)
Azimuth
Range
Figure 2. JPL AIRSAR data of Goldstone Lake area in Pauli RGB colour composites at (a)C-band, (b) L-band and (c) P-band. Azimuth ambiguities from trihedral corner reflectors areclearly shown in (a), but not in (b) or (c). The image is about 2.0 km × 3.7 km.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3203
(a) (b) (c)
Figure 3. Decomposed C-band data for part of the lake bed: (a) single, (b) double and (c)diffused. Some ambiguities from the corner reflectors are highlighted in (b).
(figure 3(a)) are trihedral corner reflectors that are scatterers with the scatteringtype of a single number of reflections. Small white spots elliptically highlighted aretheir azimuth ambiguities, which are of the scattering type with a double number ofreflections (figure 3(b)). The phase difference between the scatterer of the single anddouble-bounce scattering types is π . No ambiguities from the corner reflectors areobserved in the diffused scattering mechanism, which is related to λ3 (figure 3(c)).Briefly, with the decomposition method, we further verify that a target of strong radarbackscatter produces azimuth ambiguities, the phase difference between the target andits ambiguities is π and no ambiguities exist in the scattering mechanism related to λ3.
4.2 Kojima-wan Bay
Figure 4 shows a slant-range C-band AIRSAR image in total power. The pixel spac-ing is 4.6 m in the azimuth direction and 3.3 m in the range direction. It is about 8km wide (east–west) and 10 km high (north–south). Tamano City is on the north,Kojima-wan Bay in the middle and Sakaide City on the south. A small southern por-tion of Tamano City and northern part of Sakaide City as well as two small inlandson the east are shown. They all are of various values in radar backscatter. The studyarea outlined by a rectangle is ∼5 km wide and 6 km high. Numerous bright spots andsome spots with tails are noticeable and all could be ships, their azimuth ambiguitiesor sea clutter. Figure 5 shows the close-up view of C-HH (a), C-HV (b), C-VV (c),L-HH (d), L-HV (e) and L-VV (f ) amplitude images. Considering the C-band alone,one could conclude that all bright spots are ships because their intensities are muchhigher than those of surrounding sea clutter, even though some of the spots could bethe azimuth ambiguities. Thus, there could be many false alarms. Comparing C-banddata with L-band data spatially, one can visually delineate ships from their ambigu-ities. For example, T1 represents a ship target, outlined by rectangles in figure 5(a).
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3204 C. Wang et al.
Range
Azi
mut
h
Figure 4. AIRSAR data (C-band) covered Kojima-wan Bay, Japan, in total power. The imagecovers an area of ∼8.0 ×10.0 km2.
Circled A11 and A12 are ambiguities of T1 in the azimuth direction. Owing to differentDoppler frequency shifting of ambiguities A11 and A12, the orientation of the ambi-guity pair is not exactly along the azimuth direction or perpendicular to the rangedirection (figure 5(a)). Next, can we apply our algorithm to the polarimetric C-banddata to identify a ship from its ambiguities or sea clutter without the assistance ofL-band data?
Eigenvalues λ1, λ2 and λ3 obtained by the target decomposition method of C-bandPolSAR data are shown in figure 6. The first and second eigenvalues (λ1 and λ2) ofazimuth ambiguities (spots circled to show examples) are still high (figure 6(a) and (b)).However, for the same ambiguities, the intensities of the third eigenvalue (λ3) are muchlower than those of the first and second eigenvalues (figure 6). Also, the intensities ofthe third eigenvalue of the azimuth ambiguities are much lower than those of ship tar-gets. Furthermore, the intensities could be nearly equal to those of sea clutter, becausethey are not noticeable (figure 6(c)).
To closely examine the observed variations in the intensities of the eigenvalues, weoutlined a box with 400 pixels (in the azimuth direction) by 200 pixels (in the rangedirection). Then, we extracted the eigenvalues of individual pixels and plotted themin 3D. In figure 7(a), the intensity of the target (T1) or its ambiguity (A11) was muchstronger than that of the surrounding sea clutter. Using the value of the intensity of λ1
alone, we could derive two targets. One was a ship, but the other was not. On the otherhand, only the value of the intensity of λ3 from the target was high (cf. figure 7(c) vs.7(a)). Thus, using the polarimetric characteristics, we could differentiate the ship fromits azimuth ambiguities.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3205
A11
A12
(a)
(d) (e) (f)
(b) (c)
T1
Figure 5. AIRSAR amplitude images: (a) C-band HH channel, (b) C-HV, (c) C-VV, (d) L-HH,(e) L-HV and (f ) L-VV. Ambiguities are observed at C-HH, C-HV and C-VV image, highlightedby white ellipses. No ambiguity is observed at the L-band.
A11
(a) (b) (c)
A11
T1 T1T1
Figure 6. Eigenvalues derived from the target decomposition method: (a) λ1, (b) λ2 and (c) λ3.
To illustrate the effectiveness of the proposed algorithm, ships have been detectedfrom the above-mentioned SAR image. Figure 8(a) shows the results of the C-bandHH image using our algorithm. As a comparison, results from the two-parameterCFAR and PWF methods are also shown in figure 8(b) and (c). In figure 8, the rect-angles represent real targets, and the circles represent false alarms. The proposedalgorithm had no false alarm (figure 8(a)). However, the two-parameter CFAR (fig-ure 8(b)) and PWF (figure 8(c)) methods produce false alarms. Spatially, we could
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3206 C. Wang et al.
10
(a) (b) (c)
–10
–20
–30400
200Azimuth (pixel)
00
Ran
ge (p
ixel
)
100
200
0
10
–10
–20
–30400
2000
0
100
200
0
10
–10
–20
–30400
2000
0
0
Figure 7. Three-dimensional plots of the (a) first (λ1), (b) second (λ2) and (c) third (λ3)eigenvalues of T1 within an image window of 400 × 200 pixels.
(a) (b) (c)
Figure 8. Ship detection results of C-band AIRSAR data using (a) the proposed method, (b)the two-parameter CFAR method (HH channel) and (c) the PWF approach. The rectanglesrepresent targets and circles represent false alarms.
state that the false alarms in figure 8(b) are caused by azimuth ambiguities, as wellas speckle or sea clutter (for the false alarms that do not follow the anticipated spa-tial patterns as described in equations (1) and (2)). The false alarms in figure 8(c) aremainly caused by azimuth ambiguities.
4.3 Tokyo Bay
To evaluate the applicability of our algorithm further, we analysed another AIRSARdataset covered sea surface near Tokyo Bay. The data were acquired on 2 October2000. The pixel spacing in azimuth and slant-range are 4.6 and 3.3 m, respectively.Figure 9 shows C-HH (a) and L-HH (b) amplitude images. Also, considering the C-band alone, one could identify many bright spots as ships, even though some of thespots could be azimuth ambiguities. For instance, the intensity and shape of the ambi-guity A1 is almost the same as the target T1. However, comparing C-band data withL-band data spatially, we can visually delineate ships and identify and remove theirambiguities. Nine ships are shown in figure 9.
Figure 10(a) shows the results of the proposed algorithm, whereas outputs from thetwo-parameter CFAR and PWF methods are presented in figure 10(b) and (c). Thealgorithm produces none of the false alarms. However, the two-parameter CFAR and
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3207
T1
Range
Azim
uth
T1
A1
(a) (b)
Figure 9. AIRSAR images (HH) covered sea surface near Tokyo Bay, Japan. (a) C-band and(b) L-band. The image is about 2.6 × 2.3 km.
(a) (b) (c)
Figure 10. Ship detection results: (a) the proposed method, (b) the two-parameter CFARmethod and (c) the PWF approach.
PWF methods output multiple false alarms. Again, the azimuth ambiguities are majorcauses for the false alarms.
4.4 Anacapa Island
Figure 11 shows a slant-range C-band total power image with pixel spacing in azimuthof 4.6 m and in the range of 3.3 m. The data were acquired on 16 April 2003. Afteranalysing C-band data and the corresponding L-band data covering the same sea area,we can conclude that a bright object in the centre of figure 11 represents a very largeship. A series of bright objects above and below the ship could be first and secondazimuth ambiguities.
Figure 12 shows 3D plots of the first, second and third eigenvalues of the C-banddata. There are noticeable values contributed from the series of ambiguities for thefirst and second eigenvalues (figure 12(a) and (b)). However, there might be a lack ofsignificant values from the ambiguities in figure 12(c) of the third eigenvalue. In otherwords, the contrast ratios of the third eigenvalue to the ambiguities or background(sea clutters) are high and could be of similar values. Using eigenvalue λ3, one couldresolve the ship from its multiple azimuth ambiguities.
Figure 13 shows the results of (a) our method using PolSAR data, (b) the two-parameter CFAR method (applied to HH data) and (c) the PWF method using thepolarimetric data. It can be seen that the proposed method produces no false alarm.However, the first and second azimuth ambiguities are delineated as targets indicatedby circles in the results of two-parameter CFAR and PWF methods. Finally, there isa high possibility that the speckle or sea clutter produces a false alarm, as shown infigure 13(c) (near the edge in the middle).
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3208 C. Wang et al.
2nd ambiguity
Range
Azim
uth
1st ambiguity
1st ambiguity
2nd ambiguity
Real target
Figure 11. AIRSAR data (C-band) covered sea surface near Anacapa Island, CA, USA, intotal power. The image covers an area about 2.3 × 4.1 km.
30
20
0
–20
–40900
600300
0 0300
Azimuth (pixel)Range (pixel)
600700
30
20
0
–20
–40900
600300
0 0300
600700
30
20
0
–20
–40900
600300
0 0300
600700
(a) (b) (c)
Figure 12. Three-dimensional plots of (a) first (λ1), (b) second (λ2) and (c) third (λ3)eigenvalues of figure 11.
5. Concluding remarks
Of SAR images from seas, azimuth ambiguities of ships are often visible due to theirrelatively high intensities as compared with the low-clutter background of seas. Bothtwo-parameter CFAR and PWF methods only use statistics of surrounding sea clutter(as well as a target). Consequently, the azimuth ambiguities can be easily delineated asships and cause false alarms.
Using eigenvalue–eigenvector decomposition from PolSAR data, we have analysedthe scattering mechanisms of a ship, its azimuth ambiguities and sea clutter. Then, aship detection algorithm for airborne C-band polarimetric data has been developedbased on the difference of their scattering mechanisms. To test the applicability ofthe algorithm, AIRSAR data from the sea were analysed. Compared with the resultsof the conventional two-parameter CFAR and PWF methods, our method can effec-tively reduce the false alarms caused by the azimuth ambiguities (as well as speckle
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
Removing azimuth ambiguities 3209
(a) (b) (c)
Figure 13. Ship detection results: (a) the proposed method, (b) the two-parameter CFARmethod and (c) the PWF approach.
and/or sea clutter). Thus, the additional information contained in polarimetric datadoes improve the ability of ship detection with no or a minimum number of falsealarms.
If only intensity data are available, using cross-polarized data can and will removesome ambiguities as compared to the use of co-polarized data alone. Polarimetric dataare needed to potentially remove all ambiguities and identify all targets. Finally, wenoticed that if multi-wavelength SAR data were available, the azimuth ambiguitiescould be identified by using the differences in their spatial displacements. Thus, corre-lating a multi-wavelength SAR image spatially can be used to remove the ambiguityand identify the ship.
AcknowledgementThis work was supported by the Nature Science Foundation of China (contracts41021061 and 40901172) and the National Research and Development Programmeof Key Bases, China (2007CB714405).
ReferencesCHEN, J., CHEN, Y. and YANG, J., 2009, Ship detection using polarization cross-entropy. IEEE
Geoscience and Remote Sensing Letters, 6, pp. 723–727.CLOUDE, S.R. and POTTIER, E., 1997, An entropy based classification scheme for land appli-
cation of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 35,pp. 68–78.
CURLANDER, J.C. and MCDONOUGH, R.N., 1991, Synthetic Aperture Radar: Systems and SignalProcessing (New York: John Wiley).
ELDHUSET, K., 1996, An automatic ship and ship wake detection system for spaceborne SARimages in coastal regions. IEEE Transactions on Geosciences and Remote Sensing, 34,pp. 1010–1019.
FREEMAN, A., 1993, The effects of noise on polarimetric SAR data. Proceedings of InternationalGeoscience and Remote Sensing Symposium, 2, pp. 799–802.
FREEMAN, A., SHEN, Y. and WERNER, C.L., 1990, Polarimetric SAR calibration experimentusing active radar calibrators. IEEE Transactions on Geoscience and Remote Sensing,28, pp. 224–240.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11
3210 C. Wang et al.
GUARNIERI, M., 2005, Adaptive removal of azimuth ambiguities in SAR images. IEEETransactions on Geoscience and Remote Sensing, 43, pp. 625–633.
HARALICK, R.M., SHANMUGAN, K. and DINSTEIN, I., 1973, Textural features for image classi-fication. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, pp. 610–621.
KUO, J.M. and CHEN, K.S., 2003, The application of wavelets correlator for ship wakedetection in SAR images. IEEE Transactions on Geoscience and Remote Sensing, 41,pp. 1506–1511.
LI, F.K. and DANIEL, N., 1985, Doppler parameter estimation for spaceborne synthetic apertureradars. IEEE Transactions on Geoscience and Remote Sensing, GE-23, pp. 47–55.
LI, F.K. and JOHNSON, W.T.K., 1983, Ambiguities in spaceborne synthetic aperture radarsystems. IEEE Transaction on Aerospace and Electronic Systems, AES-19, pp. 389–396.
LIAO, M., WANG, C., WANG, Y. and JIANG, L., 2008, Using SAR images to detect ships fromsea clutter. IEEE Geoscience and Remote Sensing Letters, 5, pp. 194–198.
LIU, C. and GIERULL, C.H., 2007, A new application for PolSAR imagery in the field of movingtarget indication/ship detection. IEEE Transactions on Geoscience and Remote Sensing,45, pp. 3426–3436.
NOVAK, L.M. and BURL, B.C., 1990, Optimal speckle reduction in polarimetric SAR imagery.IEEE Transaction on Aerospace and Electronic Systems, 26, pp. 293–305.
RANEY, R.K., 1998, Radar fundamentals: technical perspective. In Principles and Applicationsof Imaging Radar: Manual of Remote Sensing, F.M. Henderson and A.J. Lewis (Eds.),3rd ed., Vol. 2, pp. 9–130 (New York: John Wiley).
SCIOTTI, M., PASTINA, D. and LOMBARDO, P., 2001, Polarimetric detectors of extended targetsfor ship detection in SAR images. Proceedings of International Geoscience and RemoteSensing Symposium, 7, pp. 3132–3134.
URATSUKA, E., KOBAYASHI, T., UMEHARA, T., SATAKE, M., NADAI, A., MAENO, H.,MATSUOKA, T., MASUKO, H., WAKABAYASHI, H. and SHIMADA, M., 2001, Combinationof two airborne SAR observations (PacRim2 over Japan). Proceedings of InternationalGeoscience and Remote Sensing Symposium, 3, pp. 1400–1402.
VACHON, P.W., THOMAS, S.J., CRANTON, J., EDEL, H.R. and HENSCHEL, M.D., 2000, Validationof ship detection by the RADARSAT synthetic aperture radar and the ocean monitor-ing workstation. Canadian Journal of Remote Sensing, 26, pp. 200–212.
WACKERMAN, C.C., FRIEDMAN, K.S., PICHEL, W.G., CLEMENTE-COLON, P. and LI, X., 2001,Automatic detection of ships in RADARSAT-1 SAR imagery. Canadian Journal ofRemote Sensing, 27, pp. 568–577.
WANG, C., LIAO, M. and LI, X., 2008, Ship detection in SAR image based on the alpha-stabledistribution. Sensors, 8, pp. 4948–4960.
WANG, Y. and DAVIS, F.W., 1997, Decomposition of polarimetric synthetic aperture radarbackscatter from upland and flooded forests. International Journal of Remote Sensing,18, pp. 1319–1332.
Dow
nloa
ded
by [
Min
gshe
ng L
iao]
at 2
0:33
11
Nov
embe
r 20
11