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
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
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
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: 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
1.3.2. Target scattering vector k The relationship between incident and scattered waves is as follows : ES
e jkr e jkr S11 S EI r r S21
S12 EI S22
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
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.
k3P .k 3*TP
and C k3L .k 3*TL
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.
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 R1s1 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
k 3L1 k *T C6 3L1 k 3L2
C *T int
T2 Cint C2
The interference equation is as follow:
arg 1k3P k 3*TP *2T 1
By using Eq. (1.85), the complex interferometric coherence as a function of the polarization of the two images may be written as: 12* 1*2 1, 2 (1.81) 11* 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.
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
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 188.8.131.52. 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
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
* , then we get 3 equations in 4 unknown equations: SHH , SVV and S HH SVV * S HH S HH fS fD 2
* S HH SVV f S f D
* 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
PS f S 1
PD f D 1
184.108.40.206. 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 , 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 *
2 0 fD 0 * 1
a 0 fV 0 1 d
1 0 fH j 2 c 1
j 2 2 2*
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.
* 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 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
The coherence matrix [T] Determined TS, TD, TV
0.3 PV 0; PD .Pt
PD 0; PV c
T13 PD 2 P T11 D
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,
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
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. Forest
The proposed method
(d) Surface scattering
(b) Freeman decomposition
(e) Double bounce scattering
(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
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*
2 * *
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.
Figure 2.11: Optical image of the survey area (a) Optical image GoogleEarth, (b) Image color of correlation coefficient A
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.
Agricultural land The proposed method
(b) Yamaguchi decomposition
(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.
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 i1 The root mean square error is defined as: N
hV i hV
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.
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).
k 1 k 2
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
11 12 21 22 0 0
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 jD TD fV e jV 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:
; V arg 1
0 ; f S 0 ; f D 22 33 11 ; D arg 22 33 11
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 jS FD f D e
jV ; FV fV e ; arcos
11 22 TV 11 TV 22 FV
12 ; 1 22 33 FD
22 TV 11 TV 22 FV
4 12 TV 12 FV
2 11 22 TV 11 TV 22 FV
TV 11 TV 22 FV
4 12 TV 12 FV
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
V arg 1
Estimating surface scattering parameters using the coherence set
33 T V 33
V 0 kZ
0 arg 2 31 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
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 220.127.116.11. 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.
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 18.104.22.168. 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.
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.
(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
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