Tải bản đầy đủ

Nghiên cứu giải pháp nâng cao độ chính xác nhận dạng và xác định tham số mục tiêu dựa trên ảnh ra đa mặt mở tổng hợp phân cực và giao thoa phân cực tt tiếng anh

MINISTRY OF EDUCATION AND TRAINING

MINISTRY OF NATIONAL DEFENCE

ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY

BUI NGOC THUY

Research ON THE ACCURACY IMPROVEMENT OF THE TARGET
PARAMETERS IDENTIFICATION AND DETERMINATION USING
POLARIMETRIC SYNTHETIC APERTURE RADAR AND
POLARIMETRIC INTERFEROMETRIC IMAGES

Specialization: Electronic engineering
Code:

9 52 02 03

SUMMARY OF DOCTORAL THESIS IN ENGINEERING

Hanoi, 2019



This thesis has been completed at:
ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY

Scientific Supervisors:
1. Assoc. Prof, Ph.D Le Vinh Ha
2. Ph.D Pham Minh Nghia

Reviewer 1: Prof, Ph.D Bach Gia Duong
University of Engineering and Technology, Vietnam
National University, Hanoi
Reviewer 2: Assoc. Prof, Ph.D Hoang Van Phuc
Military Technical Academy
Reviewer 3: Ph.D Le Thanh Hai
Academy of Military Science and Technology

This thesis was defended at the Doctoral Evaluating Council at Academy
level held at Academy of Military Science and Technology
in 8:30, date ….month ….. year 2019.

The thesis can be found at:
- Library of Academy of Military Science and Technology
- Vietnam National Library


1

INTRODUCTION
1. The urgency of thesis
Over the last two decades, with the development of science and technology,
remote sensing data has been used more widely in many fields. Remote sensing
applications have been supporting effectively in tactics and modern warfare by
creating the unexpected elements, surveillance and locate targets exactly, to name
but a few. By processing remote sensing data that are obtained from satellites and
aeronautics with very high resolution, the identification and determined of objects
such as military targets and civilian targets could provide the relative accuracy
without making physical contact with the objects.
Particularly, in order to assess the impact of changing forest ecosystems, ice,
as well as the phenomenon of global climate change, remote sensing technology
has been researched and developed and applied in varied fields of socio-economic
life. Technique of Polarimetric Synthetic Aperture Radar and Polarimetric
Interferometric (PolSAR and PolInSAR) is a topic that is drawing the attention of
scientists all over the world. This technology contributes to deal with the problems
of the object‘s identification and measuring target parameters. Therefore, the
thesis has its scientific and practical significance.
Therefore, the PhD student has chosen the topic: Research on the accuracy
improvement of the target parameters identification and determination using
polarimetric synthetic aperture Radar and polarimetric interferometric images.
2. The objective
Research scientific basis and solutions to improve the accuracy of
identification and identify parameters of natural targets, targets and forest height
estimates based on PolSAR and PolInSAR images.
3. The subject and scopes
PolSAR's target decomposition technique is based on Freeman's threecomponent scattering model and Yamaguchi's four-component scattering model.
The PolInSAR decomposition technique is based on the coherence set and the
three-component decompositon technique for PolInSAR images. From above
researchs, proposing and developing solutions and simulations for the accuracy
improvement of interpretation, identification and determination of target
parameters are studied by the thesis.
4. Research methodology
- Methods of collecting information, documents, general analysis of scientific
works and articles published in the world and in the country. Collect PolSAR and
PolInSAR image data sources related to the test area.
- Researching target decomposition techniques and building algorithm models
to improve the accuracy of identification and determination of target parameters
based on PolSAR and PolInSAR images.
- Programming technology and application of informatics technology in
building a program to perform calculations and simulations using MATLAB tool


2

combined with specialized software ENVI 5.0 and PolSARproSim, performing
verification experiments.
5. Scientific and practical significance of the thesis
- The research results of the thesis have contributed to the decomposition
technical theory based on the three-component scattering model and four
components for PolSAR image with asymmetric scattering model to improve the
ability to identify natural objects, artificial targets and to improve the accuracy of
estimating forest height in PolInSAR images.
- The research results of the thesis provide a full assessment of the scientific
basis as well as a test result of solutions to improve the identification accuracy and
identify targets that can be applied for military and civil purposes. Serving
teaching research, specialized research, realizing applied software and developing
remote sensing technology in Vietnam.
6. Structure of the thesis
The thesis is composed of the beginning, 3 chapters, the conclusion is as
follows:
Chapter 1: Overview of identification and determination of target’s
parameters based on polarized radar and polarization-interference images; Chapter
2: Propose target identification algorithm based on PolSAR image; Chapter 3:
Estimating target parameters based on PolInSAR images; Finally, the conclusions,
assessments and the issues that need further research.
Chapter 1. OVERVIEW OF IDENTIFICATION, DETERMINATION
OF TARGET PARAMETER BASED ON POLARIZED RADAR
AND POLARIZATION INTERFERENCE IMAGES
1.1. Radar equation
The electromagnetic waves of radar system transmitted in the medium can be
obtained at a target and the energy carried by the incident wave is absorbed by the
target itself, whereas the rest is reradiated as a new electromagnetic waves. Due to
the interaction with the target, the properties of the reradiated waves can be
different from those of the incident ones. From this change, they can help us
describe or identify targets. In reality, we are interested in the changes concerning
the polarization of the wave.

Figure 1.1: The interaction of electromagnetic waves with a target


3

The radar equation represents as the following:
P G ( , ) A ( , )
(1.1)
PR  T T 2  ER 2
4 rT
4 rR
1.2. The formation of Synthetic Aperture Radar image
SAR image systems allow for monitoring the earth on a global scale at all
times and in all weather conditions. The basic geometry of a SAR system is shown
in Figure 1.3.

Figure 1.3. Basic geometric
Figure 1.4. Ground range to slant
structure of a SAR space
range projection
One of the most important criteria for assessing the quality of SAR image
systems is its spatial resolution. The spatial resolution describes the visibility of
the image as much as possible so that two scattered objects can be separated in
term of spatial meaning.
The imaging SAR system is a side-looking radar sensor with an illumination
perpendicular to the flight line direction as shown in Figure 1.4.
1.3. Characteristics of target polarization.
1.3.1. Polarized information
Polarized information in backscattered waves from a given environment can
be related to: reflecting geometric structures such as shape and orientation or
geophysical structure such as moisture, surface roughness face…

(a) Polarimetric SAR (PolSAR)
(b) Single- polarization SAR
Figure 1.8: Polarizing types in remote sensing radar


4

1.3.2. Target scattering vector k
The relationship between incident and scattered waves is as follows [42]:
ES 

e jkr
e jkr  S11
S EI 
r
r  S21

S12 
EI
S22 

(1.12)

To be able to extract physical information from the 2×2 coherent
backscattering matrix or Sinclair matrix which achieved through the construction
of system vectors, we represent the Sinclair matrix with the vector V(.) as follows:
S
S   HH
 SVH

S HV 
SVV 

1
 k  V  S   Trace  S  
2

(1.13)

Where ψ is a set of 2×2 complex basic matrices which are constructed as an
orthogonal set under the Hermitian inner product.
1.3.3. Coherency matrix [T] and covariance matrix [C] polarimetric
The polarimetric Pauli coherency matrix [T] and the Lexicographic
covariance matrix [C] are generated from outer product of the associated target
vector with its conjugate transpose as.

T  

k3P .k 3*TP

and C   k3L .k 3*TL

(1.34)

Where superscript T and * denote transpose and complex conjugation and
denotes the ensemble average in the data processing, respectively.
1.4. Scattering mechanisms
- Surface scattering.
- Double-bounce scattering.
- Volume scattering.
- Helix scattering.
- Wire scattering.
1.5. Target polarimetric characterization
Symmetric scattering hypotheses about the distribution of scattering objects
will make scattering problems simple and allow for quantitative conclusions about
their scattering properties.
1.6. Polarimetric target decomposition PolSAR
Model-based decompositions for the target based on physical scattering
models to interpret the scattering process.
(1.67)
C  CS   CD   CV 
In addition, the total power P of all components can be retrieved by
summation of the power contributions PS, PD, PV, which are calculated from the
trace of the single scattering component matrices.
(1.68)
P  Tr CS   Tr CD   Tr  CV    PS  PD  PV
Finally, the normalized power of each scattering component can be obtained
by division with P. This provides the opportunity to compare the strength of the
different scattering contributions with respect to each other.


5

1.7. Interferometry Synthetic Aperture Radar
Interferential Synthetic Aperture Radar (InSAR) takes advantage of the phase
difference between two SAR complex images described from differences at
locations or at different times.

Figure 1.21: Geometric structure of interference radar
When building the complex product s1s2* for interferometry, we could cancel the
scattering phase terms and keep the geometrical phase. In effect, we now obtain a
signal phase which depends only on the difference in range between the two positions
R  R1 R 2 as follows:




 j 2 2 R1s1  
 4


j

R


 
 
 

s1  a1e
 *


(1.71)
s
s

Ae

1
2
 2

 j 2 R2 s2  


s2  a2 e 

The observed component in these interferometric radar systems is the
interference phase  and is determined as follows:
4
(1.72)
  arg  s1s2*   
R  2 N ; N  0, 1, 2,...

1.8. Polarimetric Interferometric Synthetic Aperture Radar
PolInSAR differs from conventional SAR interferometry in that it allows
generation of arbitrary interferogram and receive polarization pairs as Figure 1.22.
Using the outer product formed from the scattering vectors k1 and k2 for
images S1 and S2, we can define a 6×6 Hermitian positive semidefinite matrix [T6]
and [C6] as follow:
k 
 3 P1   k *T
T6   
  3P1
k
 3P2 

 
k3*T
P 

k 
 3L1   k *T
C6   
  3L1
k
 3L2 

 
k3*T
L 

 T1 

2



  *T




2



C1 

 C *T
  int 

  


T2  
Cint  

C2  

(1.76)

(1.77)


6

The interference equation is as follow:



  arg  1k3P k 3*TP *2T
1

2

  arg 

*T
1

 2



(1.80)

By using Eq. (1.85), the complex interferometric coherence as a function of
the polarization of the two images may be written as:
12*
 1*2
  1, 2  

(1.81)
11* 2 2*
1*T T1  1 *2T T2   2

Figure 1.22: PolInSAR acquisition geometry
1.9. Conclusion of Chapter 1
Through an overview of the target decomposition technique based on PolSAR
and PolInSAR images, the works have been published in foreign and domestic
scientific journals as well as factors affecting the problem of identification and
correctly determination the target parameters shows:
- Research on resolving disadvantages in pixels still has many negative power
components, this is the main cause leading to inaccurate identification of targets.
- Volume scattering components are often assumed to be symmetric reflective
scattering, the number of observations is limited because some assumptions must be
accepted to eliminate ambiguity, assumptions often cause the negative power in the
scattering mechanisms leads to incorrect assessment of forest height.
Therefore, the thesis is to set out 2 main problems to solve:
Building PolSAR image processing algorithms based on scattering models in
response to the requirements of interpretation capacity enhancement, target
parameters determination such as the accuracy of natural and artificial targets
identification. This problem is solved in chapter 2.
A proposed algorithm to improve the accuracy of forest height estimation
based on simulation and satellite data sources. This problem is solved in chapter 3.


7

1

Chapter 2. A PROPOSED ALGORITHM FOR IDENTIFYING
TARGET BASED ON POLSAR IMAGE
2.1 Targeted decomposition techniques based on polarized radar images
2.1.1. Technical analysis of coherence target
The scattering model after rotating an angle  can be expressed as simple
conversion as in the following expression (2.1):
sin 
cos sin   SHH SHV   cos
 S     
(2.1)



cos   SVH SVV   sin cos 
 sin
Using a unitary transformation matrix according to the parameters of the Pauli
rotation matrix, we can express them in a form of unitary vector as follows:
1

1 0
k   
2 0
0


0
cos 2
sin2
0

0
 sin2
cos 2
0

0  S HH  SVV 


0  S HH  SVV 


0  S HV  SVH 
1   S HV  SVH 

(2.2)

We find that the complex totals SHH  SVV and SHV  SVH are invariable for the
axis of rotation, which gives them a special physical meaning.
2.1.2. Technical analysis of targets according to the scattering model
The decomposition technique following the basic scattering model as shown
in Figure 2.2 including scattering of the surface, double bounce and volume
scattering:

Figure 2.2: Basic scattering models
2.1.2.1. Freeman-Durden three component decomposition
The Freeman-Durden (FDD) decomposition is a technique for fitting a
physically based, three component scattering mechanism model to the PolSAR
observations, without utilizing any ground truth measurement.
The Freeman-Durden decomposition model:
C3V  C3S  C3 D  C3V



 f  2  f  2  3 fV
D
 S
8


0

 f  *  f  *  fV
D
 S
8

0
2 fV 8
0


8

0

3 fV

fS  fD 
8 

f S   f D 

fV

(2.17)


8

Equation (2.17) gives us four equations with five unknowns. However, the
contribution of a number

fV 2 fV
3f
,
or V of volume scattering can be eliminated
8 8
8

2

2

*
, then we get 3 equations in 4 unknown equations:
SHH , SVV and S HH SVV
*
S HH S HH
 fS   fD 
2

2

*
S HH SVV
 f S   f D

(2.18)

*
SVV SVV
 fS  fD

The fS, fD coefficients and  or  parameters can be used to determine the
properties of the target. Finally, the contribution of each scattering mechanism can
be determined for the following spans:

Span  SHH  2 SHV  SVV  PS  PD  PV
2

2



PS  f S 1  

With:

2

;

2



PD  f D 1  

2

;

(2.19)

PV  fV

(2.20)

2.1.2.2. Yamaguchi decomposition
The three-component scattering model using a covariance matrix is very
effective for scattering mechanisms in PolSAR and the algorithm is only based on
*
*
symmetric reflections SHH SHV  SHV SVV  0 , which is the limitation of the FDD
*
*
technique [12], due to SHH SHV  0, SHV SVV  0 in reality.

The Yamaguchi decomposition model:

C  f S CS  f DCD  fV CV  f H CH
 2

 fS  0
 *



0 

0 

0 d

0

0

b

0

 2


0   fD  0

 *
1




0

a


0   fV  0


1
d


0

 1


0   fH  j 2


c
 1

j 2
2
2*

1 


 j 2

(2.28)



1 

In which, fS, fD, fV, fH indicate the determined scattering coefficients,
corresponding to the surface scattering, double bounce, volume and helix
scattering mechanisms.
The contribution of each scattering mechanism can be estimated as:
2
2
PS  f S 1   ; PD  f D 1   ; PV  fV  a  b  c  ; PH  f H
(2.29)
2
2
2
Pt  PS  PD  PV  PH  SHH  2 SHV  SVV
2.2. Target identification based on the three-component scattering
decomposition with an adaptive volume modelling
Adaptive algorithm solves the problem of general eigenvalues analysis and
non-negative power constraints, thereby determining the unique minimum value
for volume scattering. Finally, we determine the power of two remaining
scattering components. The generalized double-bounce scattering component
reflects the interaction of electromagnetic waves in both natural and urban areas.










9

* Proposed decomposition method:
The coherence matrix of target is analyzed into a combination corresponding
to a practical scattering mechanism.
Then [T] by given as
1
PS 
T  f S TS  f DTD  fV TV 
2 
1  
0


*

0

2

0
 P
0  D
 
0


1

 *
2


 *
 *
 

 
 a d 0

P
 *   V  d b 0 (2.40)

2 
 0 0 c 
 2 

=1+    ;   a  b  c
(2.41)
With , ,  are parameters of the surface scattering and double bounce
scattering models
When  is known, we can calculate the remaining power Pv, and PS.
.P 2

P P 
2 
PD   Pt ; PV  (T33  t .  ); PS  1    T11  D  V a 
(2.49)
c

  

Algorithm flowchart:
2

2





The coherence matrix [T]
Determined TS, TD, TV

  0.3
PV  0; PD   .Pt



PS  1  

2

  T

11 

*



T23

T33

 

;



PD 



T33
*

;

PD  0; PV   c



PS  1  

2

  T

11



PV 



  0;   0;

T13
PD 2


P
T11  D


T22 

PV
b

 
P
T11  V a

T22 

Calculate PS., PD and PV using parameters ,  and 
Figure 2.5: The flowchart of proposed algorithm
To evaluate the proposed decomposition method, the experiments were
performed using the full PolSAR data of ESAR (Experimental Synthetic Aperture
Radar) with 3x3m resolution. By testing the area near Oberpfaffenhofen,


10

Germany, Figure 2.6 (a) shows the optical image of this area. The  is the ratio of
asymmetric scattering power to total power. Figure 2.6 (c) shows the color image
of the  coefficient.

Figure 2.6: Survey area, (a) Optical image of Oberpfaffenhofen area,
(b) Pauli image, (c) Image color of correlation coefficient 
A

B

The proposed
decomposition

(a)

(b)

Freeman decomposition

Figure 2.7: Decomposition image of the test area, (a) Color image of the three
components of the proposed decomposition method, (b) Color image of the three
components of the Freeman decomposition method
* Experimental results:
The effectiveness of the proposed method is evaluated in comparison with
Freeman's three-component decomposition. From the compared results, we can
evaluate accurately with dominant double bounce scattering mechanisms in the
urban area and dominant volume scattering in the forest area. In the figure 2.7 (a),
the volume scattering component determined from the proposed decomposition
shows that the observation is clearer than that in the Freeman decomposition
technique as shown in Figure 2.7 (b). In Figure 2.7 (b) many pixels in the image of
the urban area are still green, thus lead to misinterpret and misidentify the target.
The main reason is the existence of many pixels with negative power components
in the Freeman decomposition technique.


11

In Figure 2.8, we could find that the  value is quite low corresponding to
forest area and agricultural land. However, in areas with artificial structures or
urban areas, the  value is quite large.

Figure 2.8: Graph  at the test areas (a) forest area,
(b) farmland area, (c) urban area.
Thus, the proposed algorithm has added the asymmetric double bounce
scattering component, consequently, the image results have significantly
improved, and the target identification and determination are more precicely than
that in the Freeman decomposition.
Forest

Agricultural land

Urban

The proposed method

(a)

(d)
Surface scattering

(b)
Freeman decomposition

(e)
Double bounce scattering

(c)

(f)
Volume scattering

Figure 2.9: Pie chart of three components of scattering in surveyed areas.
(a,b,c) the proposed decomposition method, (d,e,f) Freeman decomposition


12

It could be seen that the stability of the proposed decomposition method
compared to the Freeman dicomposition method is more clearly in comparing the
ratio of pixels with non-negative power components to filtering windows of
different sizes, shown in the Table 2.1.
Table 2.1: The table of the ratio of pixels with non-negative power components
Filter window size
1×1
3×3
7×7
9×9
Proposed method
99.09%
99.14%
99.20%
99.32%
Freeman method
97.05%
98.21%
98.64%
98.83%
From the above results, comparisons and analysis, the proposed method is
better and more stable than Freeman's three-component decomposition method.
Although asymmetric double bounce scattering could not completely solve all the
scattering problems in urban areas. Some pixels of buildings and the transport
system are still misinterpreted and misidentification.
2.3. Urban target identification based on four-component decomposition
technique with extended volume scattering model
In the proposed method, the thesis uses adaptive algorithms to determine the
covariance matrix parameters of volume scattering and asymmetric scattering. The
effectiveness of the proposed method is proved with PolSAR data obtained from
ESAR airborne remote sensing system.
* Proposed decomposition method:
To better describe asymmetric scattering, the method will take into account
all parameters related to asymmetric scattering information, the proposed
decomposition model is presented as follows:
C  f S CS  f DCD  fV CV  f asymCasym


 2

f S  0
 *


0
0
0

 2
 

0   f D  0

 *
1 



0
0
0

a
 

0   fV  e*

 *
1
 d


e
b
f*

d


f 

c 




f asym 




2
2

*

2 *
*

2

2

2 *




2 

1 




(2.54)

2
2
where     2  1;   a  b  c; Pasym is the asymmetric scattering
power component; 2   , 2  are both complex numbers corresponding to C12
and C23, The scattering terms can be utilized to describe the general case of
nonreflection symmetric scattering, generally, C12  0 and C23  0 and a  f are
the elements in the volume scattering model.
Compared to the Yamaguchi four component decomposition method, the
proposed decomposition method uses the asymmetric scattering mechannism.
Therefore, it can be seen that the asymmetric scattering often appears in complex
urban areas, and disappears in natural distributed areas.
* Experimental results:
The effect of the proposed algorithm is evaluated based on dataset received
from the E-SAR airbonre system and the test area which is close to
Oberpfaffenhofen, Germany.


13

Figure 2.11: Optical image of the survey area (a) Optical image GoogleEarth,
(b) Image color of correlation coefficient 
A

B

C

The proposed decomposition

(a)
(b)
Yamaguchi decomposition
Figure 2.12: Image decomposition of the test area (a) The proposed
four-component method, (b) Yamaguchi decomposition
From the comparison of image results shown in Figure 2.12 (a) we could see
that urban areas are purplish red, better than those in Yamaguchi's decomposition
in Figure 2.12 (b). From the results of the proposed decompositon method, the
target identification and determination of the proposed method are more precise
than that in the Yamaguchi decomposition technique.
The proposed method utilise an asymmetric scattering component in order to
improve the limitation of Yamaguchi decomposition in terms of the interpretation,
estimation, and classification of the terrain of the targets. From the above results,
comparisons and analysis, the proposed method shows better improvement of
Yamaguchi's four-component decomposition method. Although the proposed
method still has the disadvantage is the forest areas located bottom right image of
Figure 2.12 (a) misidentification due to asymmetric scattering component exceeds
the power level. In the future, the thesis will continue to research further
improvement and hope for better results aimed at improving the effectiveness of
the proposed method.


14

Forest

Agricultural land
The proposed method

Urban

(a)

(b)
Yamaguchi decomposition

(c)

(d)
(e)
(f)
Surface scattering
Double bounce scattering
Volume scattering
Asymmetric scattering
Figure 2.13: Pie chart of four-components of scattering in surveyed areas.
(a,b,c) the proposed decomposition method, (d,e,f) Yamaguchi decomposition
2.4. Conclusion of Chapter 2
1. The overview of the Freeman and Yamaguchi target decomposition
techniques.
2. Build two target identification models based on three-component and fourcomponent decomposition techniques with asymmetric scattering models.
3. The program is set up in Matlab environment. Then, based on the results
extracted from Matlab software, the thesis continues to use specialized software
ENVI 5.0 to perform interpretation and analysis of target.
4. Experimental results of the two proposed algorithms at the points with a
negative power component significantly reduced. Therefore, there has been a
significant improvement in the identification and interpretation of targets more
accurately than the Freeman decomposition technique and the Yamaguchi
decomposition technique.
From the results of the above study, the proposed methods show improved
accuracy in interpretation, identification and determination of target.
Chapter 3. ESTIMATING PARAMETERS OF TARGET
BASED ON POLINSAR IMAGE
3.1. Methods of height estimating of target
3.1.1. Three-stage inversion technique
Forest height estimation in three-stage inversion technique can be divided
into three separate stages.


15

The implementation of the three-stage inversion method is summarized in
Figure 3.1.

Figure 3.1: Three-stage inversion method
The forest height estimation is usually determined after eliminating the
phase component of the surface in complex interferometric coherence coefficients
of the volume scattering component in terms of an inversion space in the unit
circle. A  vol value in the inversion space can be used to recover the pair of value
of height and extintion coefficient.
The definition of average height of tree is:
1 N
hV   hV i
(3.2)
N i1
The root mean square error is defined as:
N

RMSE 

 hV i  hV

i 1

N

2

(3.4)

Where hV-i is the tree height estimation at i-th pixel, and N is the number of
samples used for the calculation.
3.1.2. Estimation of Signal Parameters via Rotational Invariance Techniques
The main idea of this method is to transform the observation space into two
interpolated subspace: signal and noise. ESPRIT algorithms can be extended to
analyze SAR data. For SAR data, the backscattering signal received from the antenna
is analyzed into a total of different scattering mechanisms. The interferometry phases
depend strongly on the observed medium.


16

2 Figure 3.2: ESPRIT algorithm
3.1.3. Coherence set theory
The continuous polarization space enables us to use the polarization diversity
and to consider the set of all coherences as a continuous entity. We will refer to
the set of all coherences in the following as the coherence set. It has been
identified that the coherence set is directly connected with the mathematical
concept of the numerical range (also known as field of values) of the contraction
matrix. This section is aim to present some theoretical aspects of the PolInSAR
coherence set. The first part of this section deals with the different definitions of
the numerical range. The second and third parts present principles for information
extraction of the coherence set geometry and the coherent properties in the
complex coherence plane.
3.2. The forest parameter estimation based on the target decomposition
technique for PolInSAR images
In the proposed method, the accuracy of forest parameter estimation can be
improved by a coherence method. First, the phase and the parameters of the
scattering object in the tree canopy are determined by the target analysis algorithm
by the adaptive scattering model, then removing the scattering component from
the covariance matrix and the phase of the terrain is determined by the ESPRIT
algorithm. Finally, the forest height is estimated and compensated by the
coherence compensation method. Experimental results show that the accuracy of
forest parameter estimation is significantly improved.
3.2.1. Polarization interference coherence
The data received from the PolInSAR system are usually represented by a
complex coherence matrix 6×6, and are represented in (3.28).


17

T   kk

 T1

 *T


*T




T2 

k 
1

 k 2 

với k  


(3.28)

The polarization interference coherence of PolInSAR system could be
described by a polarization function of two images and represented as follows:
 1*T  2
*T 
(3.29)
 1, 2 
 *T
 1*T T1 1 *2T T2 2  T 





where  1   2   is a unit complex vector of each polarization channel,
T  T1  T2  2 . The contracted form of coherence matrix for PolInSAR data is given by:
 T

1 2

T

1 2

  11  12

  21  22

 0
0


0 


0 

 33 

(3.30)

3.2.2. Scattering mechanisms for PolInSAR data
- Surface scattering
- Double-bounce scattering
- Volume scattering
3.2.3. Estimating scattering parameters of trees
The contracted coherence matrix PolInSAR's is analyzed into the sum of three
sub-matrices corresponding to the three scattering components: volume scattering,
double bounce scattering and surface scattering:
j
  f S e S TS   f D e jD TD   fV e jV TV 
(3.49)
where f S , f D .and fV represent the scattering power coefficient of single bounce,
double bounce and volume scattering respectively.
If  11 <  33 then the parameters of scattering components are determined
as follows:



fV  1  

2



11



; V  arg 1  

 

2

11



2

2

  0 ; f S  0 ; f D   22  33   11 ; D  arg  22  33   11

(3.50)



Conversely, if  11   33 , then parameters of random scattering
components from tree canopy are directly determined from (3.49)


fV  33 ; V  arg  33
T
TV 33
 v33
FS  f S e jS 
FD  f D e

jD



jV
 ; FV  fV e ;   arcos 




11   22   TV 11  TV 22  FV 





11


12
;
1
 22   33  FD 


  22   TV 11  TV 22  FV



2

 4  12  TV 12 FV



2

2
11   22   TV 11  TV 22  FV 



11   22

2

  TV 11  TV 22  FV



2

 4  12  TV 12 FV



2

(3.51)


18

3.2.3. Estimate the parameters of terrain using the coherence set
In order to reduce the computational complexity and to improve the
accuracy of the terrain parameter estimation, the thesis proposes the using of the
coherence set to directly estimate the parameters of the terrain.
Algorithm flowchart:
PolSAR
Dataset 1

PolSAR
Dataset 2

Co-register photos
Contraction matrix 

Estimating scattering parameters of
trees by 3-component technique

11  33


V  arg  1 


Estimating surface scattering
parameters using the coherence set

11  33


 33 

T

 V 33 


2
 11 



hV 

V  arg 

V 0
kZ

 V 0 





0  arg  2  31 L 

 R sin 
4 Bcos  

Figure 3.7: The flowchart of proposed algorithm
Based on the characteristics of contracted coherence matrix in (3.31), we have
a coherence set for PolInSAR data as follows:
(3.52)
app  w*T w : w*T w  1, w  3
Equation (3.52) has the same form as the numerical range of the square
3
matrix A . Therefore, the numerical range of the  matrix can also be
considered as the region of the coherence set.
The interferometric phase of the ground topography is determined as:
0  arg  2  3 1  L 
(3.53)





Finally, the forest height is determined by the differency between the
scattering phase of the canopy and the scattering phase of the ground topography,
as shown in (3.56).
  0

R sin 
(3.56)
hV  V
 V  0 
kZ
4 B cos    


19

Where  is the angle between the radiation wave and the vertical axis, R
is the distance between radar and target,  is the angle of deviation between the
baseline and the horizontal axis,  is the electromagnetic wave.
3.2.4. Experimental results
3.2.4.1. Simulation data
The simulated data obtained from PolInSAR system L-band (1.3 GHz)
with 100m horizontal and 10m vertical baseline is shown in Figure 3.8. The test
forest area has an average height of 18m on a relatively flat topography with the
forest stands occupies a 2.6 Ha area and stand density is 1000 stems/Ha.
3
Table 3.2: Forest parameter estimation from simulation data
Parameters Real value Three-stage inversion method Proposed method
hV  m
18
16.0906
17.8397
0  rad 
0.0148
0.0442
0.0158
 dB / m
0.2
0.3168
0.1687
RMSE
0
3.5006
2.3264
Table 3.2 shows the results of comparing the estimated forest parameters
from the proposed method with the three-stage inversion model.

Figure 3.8: Pauli image on RGB coding (a) Pauli image of the survey area,
(b) Graph comparing the forest height of two algorithms
Therefore, from Figure 3.8 (b) and Table 3.2 it could be confirmed that the
proposed algorithm has a higher accuracy than the three-stage inversion model.
The tree height in the testing forest area estimated from the proposed method
is presented in Figure 3.9. For having a deeper evaluation of the effectiveness of
the proposed method, the thesis randomly took 200 pixels in azimuth in the test
area. The main parameters of the forest are estimated by the proposed method
with 200 pixels as shown in Figure 3.10.


20

Figure 3.9: Forest height estimated by proposed approach
Figure 3.10 is a graph showing the values and standard deviations of the
forest parameters that are determined from the proposed method.

Figure 3.10: Estimate forest parameters from the proposed method
3.2.4.2. Space borne data
The used space borne data included two single-angle scans of TienShan area
by the SIR-C radar system. This is a mixed area of forests, agricultural areas and
roads. The radar system operates with with an incidence angle of 24.569 degree
and the baseline of 60m. The satellite image of the test area is shown in figure
3.11, the white rectangle was selected for the survey. The figure 3.12 (a) is an
optical image of the testing area, The figure 3.11 (b) is the Pauli decomposition
image of the testing area with 495x495 pixels.


21

Figure 3.11: The optical image of the test forest area. The white rectangle
represents the selected area for the forest evaluation

(a)
(b)
Figure 3.12: The Tien-Shan survey area a) Optical image,
b) Pauli decompositon image
Along the cutting path of the azimuth of the optical image (Figure 3.12), we
can identify three types of topography: the red areas denoting areas of bare land or
agricultural and roads, the green areas represent forest areas.

4 Figure 3.13: The compared graph of the height results
The Figure 3.13 is the histogram of the height results estimated by the
proposed algorithm compared to the three-stage inversion algorithm through 495
pixels and most of the forest height ranges from 10 m to 25 m.
The tree height in the testing forest area has an average height of 19m. The
estimated height from the proposed method shows that the forest elevation
fluctuates at an elevation of 18.18 m and is greater than the height of 16.46 m of
the three-stage inversion method shown in Figure 3.14.


22

(a)
(b)
Figure 3.14: The estimated forest height of two methods
(a) Three-stage inversion method, (b) The proposed method
5
Table 3.3: Forest parameters estimated from two approachs
Parameters
Three-stage inversion method
Proposed method
hV  m
16.4633
18.1842
0  rad 
-0.3110
-0.1416
 dB / m
0.1043
0.1197
RMSE
4.2142
2.1814
Table 3.3 presents the results from comparing the estimated forest parameters
of the proposed method and the three-stage inversion model.
To deeper assess the effectiveness of the proposed method, the thesis
randomly took 200 pixels in azimuth in the testing area. The main parameters of
the forest are estimated by the proposed method shown in Figure 3.17. The phase
of the ground topography changed in the range of -1 to 1 rad in figure 3.17 (b), the
random anisotropic coefficient of the canopy shown in the figure 3.17 (c) and (d).

Figure 3.17: Estimate forest parameters for the survey area


23

One of the outstanding and promising applications of PolInSAR
technology has been researched and developed, that is to classify, estimate and
determine the parameters of vegetation, forest and building height.
3.4. Conclusion of Chapter 3
1. Presentation of three methods of forest height estimation with the aim of
improving the accuracy in determining forest parameters. The forest height is
estimated based on the difference of interferometric phase between the two
scattering components: it is the scattering component directly from the ground
0 and the direct scattering component from the canopy of V.
2. The proposed method of estimating surface phase 0 is based on the
coherence set, the remaining phase of the V canopy component will be
estimated from the three-component target decomposition technique for
PolInSAR images to extract the forest height.
3. Combining theoretical and empirical research. Based on the results
achieved with the simulation data, the proposed algorithm will be applied to the
experimental data received from airbonre and satellite remote sensing systems.
In addition, they could be used to directly recover other forest parameters such
as the anisotropy, the random orientation, wave attenuation in the environment.
CONCLUSION
1. The results of the thesis
The content of the thesis "Research on the accuracy improvement of the
target parameters dentification and determination using polarimetric synthetic
aperture Radar and polarimetric interferometric images" has solved the
problem of detecting and identifying targets in natural areas, urban areas and
determining forest elevation. The thesis has studied the Freeman's threecomponent scattering model and Yamaguchi's four component scattering
model, three-stage inversion algorithm and Coherence set theory. As a result,
three basic problems solved in the thesis are:
The first problem: The thesis has proposed two adaptive three-component
algorithms and four extensions with asymmetric scattering models to enhance
the capability of target identification including natural and urban areas.
The second problem: The thesis has proposed an algorithm to improve the
accuracy of forest height estimation using PolInSAR image based on the threestage inversion model, ESPRIT algorithm and coherence set.
The third problem: Three algorithms have been tested, evaluated and
compared to previous algorithms tested with the same PolSAR data set (the
observed area of Oberpfaffenhofen city of Germany) as well as simulation and
PolInSAR satellite data. The theoretical results of algorithms have been
simulated by the actual PolSAR and PolInSAR image data, the simulation
results showed the correctness of the proposed solutions and high applicability


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