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

- Researching target decomposition techniques and building algorithm models to improve the accuracy of identification and determination of target parameters based on PolSAR and PolInSAR

BUI NGOC THUY

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

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

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 component 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

three-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

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

The radar equation represents as the following:

ER( , )( , )

T T R

A

P G P

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

structure of a SAR space

Figure 1.4 Ground range to slant

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

1.3.2 Target scattering vector k

The relationship between incident and scattered waves is as follows [42]:

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

Where superscript T and * denote transpose and complex conjugation and

denotes the ensemble average in the data processing, respectively

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

C      C S  C D  C V (1.67)

In addition, the total power P of all components can be retrieved by summation of the power contributions P S , P D , P V, which are calculated from the trace of the single scattering component matrices

P Tr C   S Tr C DTr C VP S P DP V (1.68) 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

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 *

1 2

s s 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

1 2 2

2

1 1

s a e

s s Ae R



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 k and 1 k for 2

images S1 and S2, we can define a 6×6 Hermitian positive semidefinite matrix [T6]

The interference equation is as follow:

- 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

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):

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:

Equation (2.17) gives us four equations with five unknowns However, the contribution of a number ,2

f f

or 38

V

f

of volume scattering can be eliminated

2 2

D S

D S

The Yamaguchi decomposition model:

The contribution of each scattering mechanism can be estimated as:

* Proposed decomposition method:

The coherence matrix of target is analyzed into a combination corresponding

to a practical scattering mechanism

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,

c

b a

*

22 11

2

01

D S

P T

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 

The proposed 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

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

The proposed method

Freeman decomposition

Surface scattering Double bounce scattering 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

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

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:

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