第 480 回電波研連 F 分科会
開催日: 2003 年 12 月 12 日

Ground-based Polarimetric SAR Data Processingand Monitoring of Seasonal Variations of Trees
Zheng-Shu ZHOU†Wolfgang-Martin BOERNER‡and Motoyuki SATO††Center for Northeast Asian Studies, Tohoku University, Kawauchi, Sendai, 980-8576, Japan‡University of Illinois at Chicago, Communications, Sensing & Navigation Laboratory, Chicago, USA
60607-7018
E-mail: †{zszhou, sato}@cneas.tohoku.ac.jp, ‡wolfgang.m.boerner@uic.edu

Ⅰ.INTRODUCTION
Synthetic aperture radar is usually used for airborne or space borne remote sensing [1]. SAR can also advantageously
be exploited in a ground-based radar imaging system. We call it Ground-based SAR. It is a new application of theconventional SAR expansion in the spatial domain. A few design options of Ground-based SAR systems were
proposed as monitoring tools for agriculture, terrain mapping, a variety of large man-made structures, as well as
environmental studies and ground surface deformation detection, including deformation maps of static displacementsof a variety of structures like concrete girders in a controlled environment, building models, dams, and bridges, andso on [2-4]. Most of these applications of ground-based SAR were based on interferometric techniques and weredeveloped only for narrow bandwidths. An important feature of electromagnetic radiation is its state of polarization,
and a wide range of classification algorithms and inversion techniques have recently been developed based on thetransformation of polarization state by scattering objects [1, 5]. Hence, we have extended those earlier approaches
and developed an ultra-wideband, ground-based, fully polarimetric SAR system for environmental studies.
This system makes use of a vector network analyzer, one dual-polarized broadband antenna, and a positioner. Testing
results show satisfactory polarimetric performance of developed system. Polarimetric calibration obviously improved
the features of the GB-SAR system. Using the developed SAR system, we carried out measurements on threedifferent kinds of trees in spring, summer and autumn, respectively. Then, measurements for a cherry tree in threedifferent conditions, namely in buds before blooming, blooming and voluptuous leaves, were contiguously produced.
Three-dimensional images were reconstructed from the acquired data by a series of signal processing procedure. By
implementing methods of radar polarimetry [2], the ground-based broadband SAR system can be used for monitoringchanges in tree structure characteristics, such as the growth of leaves and branches due to seasonal variations. In thisreport, we describe a ground-based broadband polarimetric SAR system with some experimental performance
examinations, present broadband polarimetric GB-SAR data processing algorithms and data interpretations of tree
monitoring.

Ⅱ. PERFORMANCE EVALUATION OF GROUND-BASED POLARIMETRIC SAR SYSTEM
Based on SAR principles and simulation results, we developed a broadband, ground-based, fully polarimetric SAR
imaging system. The ground-based polarimetric broadband SAR technique is employed for environmental studies bydetecting changes in various kinds of vegetation cover due to seasonal variations by our research group [6, 7]. The
radar system consisted of a vector network analyzer, a diagonal dual polarized broadband horn antenna, an antenna
positioner unit, and a PC-based control unit. The network analyzer, operated in a stepped frequency continuous-wave
mode, was used to generate the transmitting signal and to detect scattered signals both in amplitude and phase. The
synthetic aperture is realized by scanning the antennas on a horizontal rail and moving along a vertical post. The
horizontal and vertical scanning aperture widths determine the horizontal and vertical resolutions. The range
resolution depends on the radar frequency and bandwidth [7].
Firstly, we used a metallic sphere as a calibrator to test the radar system. The measurement was carried out in outdoor
condition and some absorbing materials were used for reducing reflection from target stand. The theoreticalnormalized radar cross section (NRCS) value shown as the solid line in Fig. 1. The measured data of HH, VV and
VH components, compensated by the gain factor of the antenna used in the system, are also plotted in same figure
synchronously. We can observe that there are the same number of peaks and that the resonance points of themeasured curves coincide with those of the theoretical value shown in Fig. 1. Hence, the measured backscattering
data of co-polarization components are consistent and very similar to the theoretical value. The cross-polarization
component is about 12 dB lower than co-polarization components.
A modified polarimetric RCS calibration technique using two orientations of the dihedral corner reflectors as
calibration targets is introduced [7, 8]. This method is valid for any monostatic or quasi-monostatic radar system.
From the theoretical value of two calibrators and measured data, we can recover calibration coefficients. Using thecalibration coefficients, the calibrated scattering matrix can be calculated. Table 1 gives the measured scattering1matrices of three standard reflectors and their calibrated scattering matrices. We can find the improvements after
calibration, especially in the phase term.
Table 1 Comparison of calibrated scattering matrices with measured scattering matrices
Measured scattering matrix

Calibrated scattering matrix

Verticaldihedral

0.9813e− j113.7 0.0365e j129.4 
Sm1 = 
j129.4o1
0.0365e1.0146e− j179.11 0.0657e− j101.54 
S c m1 = − j102.39o1
0.0657e


45o dihedral

0.2977e j120.4 1
Sm2 = o10.2891e j112.3 0.1381e− j103.82 1
S cm2 = oo0.9904e− j1.43 0.1285e− j 43.20 oSphereooo1Sm3 = j 91.76o0.0677eoo0.2331e j112.83 o0.8252e − j106.83 o1
S c m3 = 
j 32.18o0.0806e

0.2454e − j 23.11 o0.9345e − j 21.96 oⅢ. THREE DIMENSIONAL IMAGING FOR TREE MONITORING
Due to the fact that trees are some of the most important vegetation scatterers in environmental studies, continued
data acquisitions were carried out for three different types of trees in three seasons, and for a cherry tree during
different situations at two experimental sites. The experimental sites are both located at the Kawauchi Campus,
Tohoku University. The experimental site with three different trees is called Exp#A. The main targets are denoted by
T1, T2 and T3, and represented three different kinds of trees. We have carried out three measurements at the same
exact position for the three different trees in late spring (April 19, 2002), in early summer (May 28, 2002) and in lateautumn (November 11, 2002), respectively. Table 2 shows the measurement conditions and situations of targets. We
used the frequency range from 1 GHz to 5 GHz. It almost covers the L-band to C-band.
Table 2 Condition and target situations for measurements of different tree types
Measurement number

Exp#A-1

Exp#A-2

Exp#A-3

SeasonSpringSummerAutumnWeather

Fine

Fine & light windy

Sunny / cloudy &windyTree 1
Japanese Zelkova

Bare twigs with bud

Flourishing large
leavesLeaves almost fall off

Tree 2
Japanese cedar

Evergreen

Evergreen

Evergreen

Tree 3
Azalea&Honeysuckle

Bush with fresh leaves

With dense plants

With a few leaves

The target on the second site, Exp#B, is a Yoshino cherry tree, one well-known kind of tree in Japan. We have carriedout three measurements for a cherry tree too. The three measurements were produced on April 02, 2003 just before
the flowers bloomed, on April 14, 2003 with very blooming flowers but no leaves, and on May 27, 2003 with all
flowers gone and substantial growth of leaves, respectively. Experimental conditions and target situation for thecherry tree is shown in Table 3. Three polarimetric scattering matrix element data sets, those of HH, VH and VV,
were acquired simultaneously.
Table 3 Condition and target situations for measurements of a cherry tree
Measurement number

Exp#B-1

Exp#B-2

Exp#B-3

SeasonSpringLate spring

Early summer

Weather

Fine

Fine

Fine & slightly windy

Yoshino cherry tree

Bare stems with buds

Blooming flowers

Exuberant leaves

3-D Data Processing Algorithms
For the acquired broadband ground-based polarimetric SAR data, a series of signal processing methods are applied.
Due to different data structure, ground-based SAR signal processing is not the same as for airborne SAR dataprocessing. First, the acquired frequency domain data are filtered by the broadband-pass filter; then transformed tothe time domain by IFFT. By selecting the interesting range at time axis, time gating is employed to the time domain
data; then back-transformed to the frequency domain by FFT. The frequency domain data are calibrated by radarsystem calibration coefficients. Thereafter, the calibrated data are compressed by matched filtering, in which the2reference signal was the reflection from an aluminum plate measured by the same radar system and parameters. Afterusing the IFFT again, the time domain data are obtained. Finally, diffraction stacking is applied to migrate the time
domain data while antenna radiation directivity compensation is done synchronously; and the special domain data
sets are obtained.
A migration method of diffraction stacking has been employed to reconstruct the image [7]. It is a very convenient
and traditional method, which is commonly used for seismic data processing. The goal of migration is to make thestacked section appear similar to the real location. This approach is an integral solution of the wave equation. It can
extrapolate wave fields of an observation aperture to larger 3-D space. Diffraction stacking migration produces one
migrated sample (a pixel) at a time by computing a diffraction shape for a scatter point at that location, summing andweighting the input energy along a diffraction path, and placing the summed energy at the scatter point location on
the migrated section. It makes the data synthesis consistent with the real conditions of the radar system measurement.
The short time Fourier transform (STFT), also referred to as the Windowed Fourier Transform, was developed as an
attempt to overcome the lack of information on time locality in Fourier analysis [9]. It is a compromise between thetime and the frequency view of a signal, and is thereby better suited for analysis of non-stationary signals than theordinary Fourier Transform is. STFT computes a time-frequency distribution of an input signal within a given
frequency interval as a sequence of short-time spectra of windowed signal segments with constant length.

Reconstructed 3-D Images
Based on measured data of HH, VH and VV polarization, at experimental site Exp#A, we have obtained spatial
domain data by above processing. Fig. 2 shows the 3-D reconstructed images of HH, VH and VV polarization by
three experiments, respectively. Analyzing the three polarimtric images of each measurement, we can find the entire
differences among the different polarizations. The HH image shows the reflection from trunks, some horizontal
branches and ground clutter, the VH image indicates strong reflections from the leaves and some slant branches andthe VV image illustrates the reflections from vertical trunks and branches. Hence, the polarimetretric performance
was demonstrated effectively.
Comparing the corresponding images of the different measurement in Fig. 2, we can find the reflections from thetrunks and the leaves are different by the seasonal variation. There are stronger reflections in the images of Figs.
2(b1), 2(b2) and 2(b3) due to the exuberant branches and leaves in summer. Moreover, we can find the clearer
ground surface in Figs. 2(c1), 2(c2) and 2(c3) in autumn, which agreed well with the real situation. The differences
have existed between Figs. 2(a1) and 2(c1), 2(a3) and 2(c3), too. Hence, we can assume that the main reflections
were caused by the volume scattering due to the different volumes of leaves in variant season. Although the shapes oftrees were very similar in spring and autumn, but the water content of trees was higher than that in autumn. Thatcaused the slight differences between the reconstructed polarimetric images in spring and autumn. The different
scattering performances of the variant trees in the different seasons can be observed generally.

Ⅳ. DATA INTERPRETATION WITH SAR POLARIMETRIC ANALYSIS
Polarimetric Power Density Images from Covariance Matrix
For deterministic scatterers, deterministic scattering or completely polarized scattering can be completely describedby the coherent Sinclair matrix. But for partial scatterers, random scattering or partially polarized scattering, they can
not be described by the Sinclair matrix. The statistical descriptions are necessary such as using the Kennaugh or theCovariance matrices. Hence, the Coherency matrix and the Covariance Matrix maybe usefully employed as target
polarimetric descriptors [10].
This coherent Sinclair scattering matrix S is a function of frequency and relative viewing angle.
S
S =  HH
 S VHS HV 
S VV 

(1)
The covariance matrix is based on the components of the scattering matrix (1). We have limited our focus to thebackscatter (or monostatic) case only. Therefore, the Covariance matrix is defined as:
CVH = hh*T**
 < S HH 2 >< 2 S HH SVH> < S HH SVV
> 


2
*
*
= < 2 SVH S HH >< 2 SVH >< 2 SVH SVV>2*
 < S S* >< 2 SVV SVH >< SVV > 
VV HH



(2)

The superscript * denotes the complex conjugate. The superscript T means transpose.
Complex spatial frequency domain data are produced from migrated spatial domain data by FFT. Hence, thepolarimetric power density images based on diagonal terms of the covariance matrix of (2) for Exp#B-2 data (cherry
flowers) and Exp#B-3 (cherry leaves) are shown in Fig. 3. The scene of cherry flowers and leaves are shown in Fig.
3

3(a) and 3(f). Power density images of cherry flowers with frequencies of 1.5 GHz, 2.5 GHz, 3.5 GHz and 4.5 GHz
are shown on the left side of Fig. 3. The scene and images of cherry leaves are shown on the right side of Fig. 3. In
lower frequencies images (1.5 GHz and 2.5 GHz), the red color and blue color (co-polarization components) are
dominant. The reason is that the lower frequency electromagnetic wave can propagate into the canopy of the cherry
tree and reflect back against large horizontal and vertical branches. With frequency increased, the green color appears
more and more, and becomes dominant. The fact is the higher frequency electromagnetic wave return from thesmaller scatterers like inclined twigs, flowers or leaves. Comparing images of flowers and leaves, we can find that alittle more green color appear in cherry tree leaves images of higher frequencies ( 3.5 GHz and 4.5 GHz). The reason
may be assumed that the volume of the cherry tree leaves is greater than that of cherry flowers and there are larger
scattering surface areas with randomly oriented leaves.

Ⅴ. CONCLUSIONSIn this report, the development of a broadband ground-based polarimetric SAR system and its application to tree
monitoring are presented. Testing results and polarimetric calibration showed satisfactory polarimetric performances
and improvement of the developed system.
Algorithms of time gating, matched filtering and diffraction stacking were very effective techniques forground-based SAR data processing. Reconstructed 3-D images of HH, VH and VV polarization components showed
good consistency with ground-truths and polarimetry theory. Hence, radar polarimetry provided valuable scattering
information about targets, particularly the scattering features of trees in different seasons, so that the different
components of trees in different periods could be distinguished.
Different analysis tools for viewing and interpreting broadband polarimetric SAR data were used. Those could
indicate scattering mechanisms for vegetation. Scattering mechanisms of different components of vegetation, e.g.
trees, were demonstrated by polarimetric analysis techniques. We also discussed, using different data acquired for
different cases to interpret the amount of diffuse scattering, for instance, for buds, flowers and leaves. It showed thatthey all provide similar information but different features could be identified by polarimetric analysis.
It was demonstrated that the broadband ground-based polarimetric SAR system presents advantages for vegetation
monitoring as well as environmental studies for a great variety of vegetation structures. At the same time, aground-based SAR system also can be used as ground truth demonstration tool for airborne SAR and space-borne
SAR in a great variety of applications.

ACKNOWLEDGEMENT
A part of this work was supported by the Grant-In-Aid for Scientific Research (S)14102024.

REFERENCES[1]

W.-M. Boerner et al, “Polarimetry in Remote Sensing: Basic and Applied Concepts,” Chapter 5 in Manual of Remote
Sensing, 3rd ed., vol. 2, F M Henderson, A J Lewis Ed. New York: Wiley, pp. 271-358, 1998.
[2] D. Tarchi, E. Ohlmer and A. Sieber, “Monitoring of Structural Changes by Radar Interferometry,” Research in
Nondestructive Evaluation. vol. 9, pp. 213-225, 1997
[3] M. Pieraccini, G. Luzi and C. Atzeni, “Terrain Mapping by Ground-Based Interferometric Radar,” IEEE Trans. Geosci.
Remote Sensing, vol. 39, no. 10, pp. 2176-2181, Oct. 2001
[4] D. Tarchi, H. Rudole, M. Pieraccini and C. Atzeni, “Remote Monitoring of Buildings Using a Ground-Based SAR:
Application to Cultural Heritage Survey,” Int. J. Remote Sensing, vol. 21, no. 18, pp. 3545-3551, 2000
[5] E. Pottier, S. R. Cloude and W.-M. Boerner, “Recent Development of Data Processing in Polarimetric and Interferometric
SAR; Invited Paper, Radio Science Bulletin,” (ed. R.W. Stone), Special Issue on URSI-GA-03; Maastricht; NL, RSB-304,
March 2003, pp. 48-59, ISSN 1024-4530.
[6] Z.-S. Zhou, and M. Sato, “Ground-Based Polarimetric SAR Systems for Environment Study”, IEEE AP-S 2003 Digest, vol.
1, pp.202-205, Columbus, June 2003.
[7] Z.-S. Zhou, Application of a Ground-based Polarimetric SAR System for Environmental Study, Doctoral Dissertation of
Tohoku University, Japan, August 2003.
[8] J.-R. J. Gau and W. D. Burnside, “New polarimetric calibration technique using a single calibration dihedral,” IEE Proc.
Microw. Antennas Propag., vol. 142, no. 1, February 1995, pp. 19-25.
[9] G. F. Boudreaux-Bartels, “Mixed Time-frequency Signal Transformations,” Chapter 12 in The Transforms and Applications
Handbook, 2nd ed., A. D. Poularikas Ed, Boca Raton, Florida: CRC Press LLC, 2000, pp. 12.6–12.8.
[10] S. R. Cloude and E. Pottier, “A Review of Target Decomposition Theorems in Radar Polarimetry,” IEEE Trans. on Geosc.
and Remote Sensing, vol. 34 no. 2 , pp. 498-518, March 1996.

4

Fig. 1 Theoretical normalized RCS curve and measured values of a metallic sphere: solid line- theoretical curve,
dotted line- HH polarization, dashed line- VV polarization and dash-dot line- VH polarization.

(a) Spring(b) Summer(c) Autumn(a1) HH (amplitude: 5.5x10-3 )(b1) HH (amplitude: 5.5x10-3 )(c1) HH (amplitude: 5.5x10-3 )(a2) VH (amplitude: 1.2x10-3 )(b2) VH (amplitude: 1.2x10-3 )(c2) VH (amplitude: 1.2x10-3 )(a3) VV (amplitude: 5.5x10-3 )(b3) VV (amplitude: 5.5x10-3 )(c3) VV (amplitude: 5.5x10-3 )Fig. 2 Test scenes and 3-D images of different trees in: (a) spring, (b) summer and (c) autumn.
5(a) Imaging area of flowers(f) Imaging area of leaves(b) 1.5 GHz(g) 1.5 GHz(c) 2.5 GHz(h) 2.5 GHz(d) 3.5 GHz(i) 3.5 GHz(e) 4.5 GHz(j) 4.5 GHz

Fig. 3 Single-frequency power density images of cherry blossom and leaves red-|HH|2 green-2|VH|2 blue-|VV|2: (a)imaging area of flowers, (b) 1.5 GHz, (c) 2.5 GHz, (d) 3.5 GHz, (e) 4.5 GHz images of flowers, and (f) imaging areaof leaves, (g) 1.5 GHz, (h) 2.5 GHz, (i) 3.5 GHz, (j) 4.5 GHz images of leaves.

6