PIERS ONLINE, VOL. 4, NO. 1, 2008 140

Comparison of Methods for Target Detection and ApplicationsUsing Polarimetric SAR Image

Lamei Zhang, Junping Zhang, Bin Zou, and Ye Zhang

Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin 150001, China

Abstract— Polarimetric SAR (PolSAR) is sensitive to the orientation and characters of objectand polarimetry could yield several new descriptive radar target detection parameters and leadto the improvement of radar detection algorithms. Target decomposition theory has been usedfor information extraction in PolSAR, and it can also explore the phase message in PolSARdata. In this paper, a comparison of polarimetric target decomposition methods is proposed.We generate a validity test for these methods using DLR ESAR L-band full polarized data.Results show that among many target decomposition algorithms, the coherent and incoherentformulations are quite comparable in distinguishing natural targets and man-made buildings.Pauli decomposition, Cameron decomposition and Freeman decomposition are suitable for thedetection of natural targets. On the other hand, SDH decomposition, OEC decomposition, andFour-component model, in particular, are very useful for man-made target extraction.

1. INTRODUCTION

Target detection using Synthetic Aperture Radar (SAR) has attracted much attention for bothcivilian and military applications. Polarimetric SAR (PolSAR) is a well established technique,which allows the identification and separation of scattering mechanisms in the polarization signaturefor purposes of classification and parameter estimation. PolSAR is sensitive to the orientation andcharacters of object and polarimetry could yield several new descriptive radar target detectionparameters and lead to the improvement of radar detection algorithms.

The polarimetric information of target echo can reflect the geometry structure and physicalcharacteristic of target, and polarimetric target decomposition theorem expresses the average mech-anism as the sum of independent elements in order to associate a physical mechanism with eachcomponent. Unlike method using SAR for information process, target decomposition explores phasemessage contained in PolSAR data. Polarimetric target decomposition theorems can be used fortarget classification or recognition.

Polarimetric SAR data are coherent by nature of the principle of operation, however, most oftenincoherent approaches are chosen for the post-processing in order to apply conventional averagingand statistical method. At present, two main classes of decomposition can be identified. One,called coherency decomposition, deals with decomposition of the scattering matrix, while another,called incoherent decomposition, deals with decomposition of coherency or covariance matrices.

The main purpose of this paper is to examine the possibilities of target detection using PolSARdata and to compare the effectiveness of target decomposition algorithms using full polarized SARimage.

2. EXPERIMENTAL DATA

The DLR ESAR L-band full polarized image of Oberpfaffenhofen Test Site Area (DE) of Germany,obtained on September 30th, 2000, was used to validate the comparison of the decompositionmethods. Its spatial resolution is 3m× 3m. The optical image and HH channel image are shownin Fig. 1(a) and (b) respectively. The test area, composed of 2816 × 1540 pixels, mainly includesforest, several kinds of farmland, bituminous macadam, and man-made buildings.

3. COHERENT TARGET DECOMPOSITION

In this category, we have study three main coherent decomposition theorems, commonly referredto as Pauli decomposition, SDH (Sphere, Diplane, Helix) decomposition, and SSCM (SymmetricScattering Characterization Method), respectively.

PIERS ONLINE, VOL. 4, NO. 1, 2008 141

(a) (b)

Figure 1: Test data of ESAR, (a) HH channel image, (b) optical image.

3.1. Pauli DecompositionThe most common known and applied coherent decomposition is Pauli decomposition. Wherebythe scattering matrix [S] can be written as:

[S] = α

[1 00 1

]+ β

[1 00 −1

]+ γ

[0 11 0

](1)

where α = (shh + svv)/√

2, β = (shh − svv)/√

2, and γ =√

2shv are the complex quantities rep-resenting, respectively, single-bounce, double-bounce, and 45◦ rotated double-bounce scatteringcomponents. Fig. 2 is Pauli decomposition of the test data. Seen from Fig. 2, Pauli decompositioncan distinguish natural target well, however, this method cannot detect man-made target.

Figure 2: Pauli decomposition, the image is colored by α (red), β (green), and γ (blue).

3.2. SDH DecompositionAccording to Krogager, the complex symmetric scattering matrix can be decomposed into threecomponents on the circular basis, which corresponding to a sphere, a diplane and a right- or left-wound helix, respectively [1]:

[S(r,l)

]= ejϕ

{ejϕsks [Ss] + kd [Sd] + kh [Sh]

}(2)

The coefficients are easily obtained from the elements in the circular basis. Thus, ks = |srl|, forthe left-wound, k+

d = |sll|, k+h = |srr|− |sll|, while for the right-wound, k−d = |srr|, k−h = |sll|− |srr|.

Because helix scattering is general scattering mechanism, which appears in an urban area whereasdisappears for almost all natural distributed scattering, SDH decomposition can distinguish man-made target from natural target well. However, SDH cannot divide one kind of man-made targetfrom another kind. Fig. 3 is SDH decomposition of the test data.

3.3. Cameron Decomposition and SSCMUnder reciprocity conditions, coherent scattering can be decomposed into a maximized symmetriccomponent and an asymmetric component by using Cameron’s decomposition theory [2]

~S = A[cos τ ~Smax

sym + sin τ ~Sminsym

](3)

PIERS ONLINE, VOL. 4, NO. 1, 2008 142

Figure 3: SDH decomposition, the image is colored by ks (red), kd (green), and kh (blue).

The maximum symmetric component⇀

Smax

sym is characterized by the two complex entities α andε, which represent the distribution of the largest symmetric scattering component on the basis ofthe orthogonal vectors,

⇀

Smax

sym = α⇀

Sa + ε⇀

Sb (4)

where⇀

Sa and⇀

Sb are trihedral and dihedral scattering vectors respectively.R. Touzi proposed SSCM to improve Cameron decomposition. Then

⇀

Smax

sym can be expressed as[3],

⇀

Smax

sym = expjφSa ·√|α|2 + |ε|2 ·

[cos(η) · ⇀

Sa + sin(η) expj(φSa−φSb) ·⇀Sb

](5)

where, η ∈ [0, π/2] characterizes the direction of scattering vector on the⇀

Sa −⇀

Sb basis. Under co-herent conditions, it provides information about the type of scattering of the maximized symmetriccomponent. φSb

− φSa∈ [−π, π] is the phase difference of

⇀

Sa −⇀

Sb channel. It can provide usefulinformation about the illuminated target if the channel coherence is high. Both the magnitude andphase of the channel coherence are worth investigating target scattering characterization.

Figures 4(a) and (b) is the distribution of η and φSb− φSa

, respectively. Different kinds offarmlands have distinct difference in φSb

−φSaaround −3π/4 and 3π/4, forest area and man-made

buildings concentrated on −π/4. Therefore the phase difference can be used to distinguish naturaltargets from man-made targets. The same to SDH decomposition, this method cannot distinguishforest and buildings, either.

(a) (b)

Figure 4: Distribution of η and φSb− φSa

in SSCM, (a) distribution of η, (b) distribution of φSb− φSa

.

4. INCOHERENT TARGET DECOMPOSITION

In this category, we discuss three incoherent decomposition theorems, there as, Freeman decompo-sition, OEC decomposition, and Four-component model, respectively.

4.1. Freeman Decomposition

Freeman proposed a three-component scattering model in which covariance matrix [C] of polari-metric SAR data is decomposed for information extraction [4]. Freeman decomposition describes

PIERS ONLINE, VOL. 4, NO. 1, 2008 143

scattering mechanisms as due to three physical mechanisms, namely surface scattering, double-bounce scattering and volume scattering:

[C] = fs[Csurface] + fd[Cdouble] + fv[Cvolume] (6)

According to this model, the measured power P may be decomposed into three quantities:

Ps = fs(1 + |β|2), Pd = fd(1 + |α|2), Pv = 83fv

P = Ps + Pd + Pv(7)

The three-component scattering model based on covariance matrix has been successfully appliedto decompose PolSAR image under the reflection symmetry condition 〈shhs∗hv〉 ≈ 〈svvs

∗hv〉 ≈ 0.

This method is based on simple physical scattering mechanisms (surface scattering, double-bouncescattering, and volume scattering), just as shown in Fig. 5, the contributions of each of the threescattering mechanisms to the total power are shown for each pixel, with surface scattering coloredblue, volume scattering green, and double-bounce scattering red. Result in Fig. 5 shows thatvolume scattering meets the observation for forest very well. Farmland has surface scatteringand double-bounce scattering dominant. This can be interpreted as indication that the longerwavelengths can penetrate the relatively short vegetation in farmland area and the backscatteris mostly from the underlying ground. Therefore, Freeman decomposition can describe differentnatural targets very good and is powerful for PolSAR image decomposition for natural distributedtarget areas. However, man-made buildings are also present the volume scattering, thus this modelcannot distinguish forest and man-made buildings.

Figure 5: Freeman decomposition, the image is colored by Pd (red), Pv (green), and Ps (blue).

4.2. OEC DecompositionFor urban area, the reflection symmetry condition does not hold, it is necessary to take the effectof 〈shhs∗hv〉 6= 0 and 〈svvs

∗hv〉 6= 0 into account. In 2005, Moriyama proposed a model for urban

area information extraction [5]. The model decomposes the covariance matrix into three kinds ofscattering mechanisms: odd-bounce scattering; even-bounce scattering and cross scattering:

[C] = fodd[Codd] + feven[Ceven] + fcross[Ccross] (8)

where fodd, feven and fcross represent the weight of odd bounce scattering, even bounce scattering,and cross scattering, respectively. [Codd], [Ceven] and [Ccross] represent the corresponding covariancebase, respectively. Then the power of each term Podd, Peven and Pcross can be calculated respectively.P is the total power of the three scattering.

Podd = fodd(1 + |β|2)Peven = feven(1 + |α|2)Pcross = fcross(1 + |γ|2 + 2 |ρ|2)P = Podd + Peven + Pcross

(9)

In urban area, buildings have strong even-bounce scattering characteristics, and their informa-tion can be easily extracted from even-bounce scattering component. Fig. 6 shows OEC decomposedimage. Comparing Fig. 6 with Fig. 1(b), we can find most of farmlands are mainly even-bouncescattering and some farmlands in the middle are mainly odd-bounce scattering. Fig. 6 shows that

PIERS ONLINE, VOL. 4, NO. 1, 2008 144

even-bounce scattering is the dominant scattering mechanism in urban areas. Therefore, this modelis suitable to extract polarimetric feature of urban areas. The forest area has a mixed color whichmeans odd scattering and even scattering are contained. The analysis shows that different targetsmay have dissimilar scattering components. Because the longer wavelengths can penetrate canopyand the backscatter is mostly double-bounce from the ground-trunk interaction. So at L band,forest also indicates even scattering in OEC decomposition. As known buildings are mainly evenscattering, therefore, OEC decomposition cannot distinguish forest and buildings well.

Figure 6: OEC decomposition, the image is colored by Pe (red), Pc (green), and Po (blue).

4.3. Four-component Scattering ModelYamaguchi proposes a four-component scattering model based on Freeman’s three-componentmodel, in which, covariance matrix [C] or coherency [T ] can be denoted as four scattering mecha-nisms [6, 7]:

[C] = fs[Csurface] + fd[Cdouble] + fv[Cvolume] + fc[Chelix] (10)

where fs, fd, fv and fc are the expansion coefficients to be determined. [Csurface] and [Cdouble] areidentical with those in Freeman decomposition, [Cvolume] is modified with 10 log

(⟨|shh|2⟩/⟨|svv|2

⟩),

and [Chelix] is introduced for encountering the helix scattering power contribution. The scatteringpowers, Ps, Pd, Pv and Pc corresponding to surface, double bounce, volume and helix contributions,respectively, are obtained as:

Ps = fs(1 + |β|2) Pd = fd(1 + |α|2)Pv = fv Pc = fc

P = Ps + Pd + Pv + Pc

(11)

Compared with three-component scattering model, the helix scattering power, correspondingto 〈shhs∗hv〉 6= 0 and 〈svvs

∗hv〉 6= 0, is introduced for more general scattering mechanism as the

fourth component, which often appears in complex urban areas whereas disappears in almost allnatural distributed scenarios. This term is essentially caused by the scattering matrix of helicesand is relevant for the complicated shapes of man-made structures, which are predominant in urbanareas. Furthermore, the volume scattering component for vegetation is modified by a change ofthe probability density function for the associated orientation angles, and the choice between the

(a) (b)

Figure 7: Four-component decomposed image, (a) the image is colored by Pd (red), Pv (green), and Ps

(blue), (b) helix scattering power Pc.

PIERS ONLINE, VOL. 4, NO. 1, 2008 145

symmetric and the asymmetric covariance can be determined by 10 log(⟨|shh|2

⟩/⟨|svv|2

⟩)of the

image.The decomposed result of the covariance matrix with Ps (blue), Pd (red), and Pv (green) is

shown in Fig. 7(a). It is seen in Fig. 7(a) that Pv (green) is especially strong in the forest area.Most farmland in blue indicates that there is no other scattering mechanism except single bouncescattering. Some farmland in pink indicates that both surface scattering and double-bounce scat-tering exist. Overall, the decomposition result is acceptable. In urban area, when orientation ofbuilding blocks is not parallel to the flight path, these areas with skew-oriented buildings produce arather predominant HV component. Therefore, the helix scattering component Pc appears strongin these areas as shown in Fig. 7(b).

Table 1: Comparison of coherent and incoherent polarimetric decomposition.

Type of decomposition model Merits Demerits

Pauli can distinguish natural target well cannot detect man-made target

SDHcan distinguish man-made target from

natural target well

cannot divide one kind of

man-made target from another kindCoherent

Cameron and

SSCM

better exploit the information provided by

the maximized symmetric scattering

component of coherent targets

not suitable for complex scenario

containing a lot of asymmetric

targets

Freeman

can describe different natural targets very

good and suitable for analyzing the natural

distributed target areas

cannot distinguish forest and

man-made buildings

OECeffective to extract polarimetric feature from

urban areas

cannot distinguish forest and

buildings well Incoherent

Four-component more general scattering model for both

natural targets and man-made targets

5. DISCUSSION AND CONCLUSION

Table 1 gives a comparison of coherent and incoherent polarimetric decomposition. It can be foundthat the coherent and incoherent decomposition models have quite comparable results in distin-guishing natural targets and man-made buildings. Pauli decomposition, Cameron decompositionand Freeman decomposition are suitable for description of natural targets. On the other hand,SDH decomposition, OEC decomposition, and Four-component model, in particular, are better indescribing man-made targets, since they take helix scattering mechanism into consideration, whichis a general scattering in urban area. Since the result is obtained only with one test data, the con-clusion has certain limitation. A general preference for these methods in other test should thereforebe further challenged by follow-on studies.

ACKNOWLEDGMENT

This work is supported by National Natural Science Foundation of China, No. 60672091.

REFERENCES

1. Krogagar, E. and Z. H. Czyz, “Properties of the sphere, diplane, helix (target scattering matrixdecomposition),” Proc. JIPR-3, J. Saillard, et al., Eds., 106–114, Nantes, France, March 21–23,1995.

2. Cameron, W. L., N. N. Youssef, and L. K. Leung, “Simulated polarimetric signatures ofprimitive geometrical shapes,” IEEE Trans. on GRS, Vol. 34, No. 3, 793–803, 1996.

3. Touzi, R. and F. Charbonneau, “characterization of target symmetric scattering using polari-metric SARs,” IEEE Trans. on GRS, Vol. 40, No. 11, 2507–2516, 2002.

4. Freeman, A. and S. L. Durden, “A three-component scattering model for polarimetric SARdata,” IEEE Trans. on GRS, GRS-36(3), 963–973, 1996.

5. Moriyama, T., T. Uratsuka, S. Umehara, T. et al., “Polarimetric SAR image analysis usingmodel fit for urban structures,” IEICE Trans. Commun., E88-B(3), 1234–1242, 2005.

6. Yamaguchi, Y. and T. Moriyama, “Four-component scattering model for polarimetric SARimage decomposition,” IEEE Trans. on GRS, Vol. 43, No. 8, 1699–1706, 2005.

7. Yamaguchi, Y., Y. Yajima, and H. Yamada, “A four component decomposition of polsar imagesbased on the coherency matrix,” IEEE GRSL, Vol. 3, No. 3, 292–296, 2006.