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Model-based Polarimetric Decomposition using PolInSAR Coherence

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Presentation on theme: "Model-based Polarimetric Decomposition using PolInSAR Coherence"— Presentation transcript:

1 Model-based Polarimetric Decomposition using PolInSAR Coherence
Si-Wei Chen, Motoyuki Sato Tohoku University, Japan Thank you, Mr. chairman. Today my topic is Model-based Polarimetric Decomposition using PolInSAR Coherence. I am Chen Siwei, from Tohoku University, Japan.

2 Outline Introduction PolInSAR Coherence Proposed Decomposition
Current model-based decompositions Limitations PolInSAR Coherence Estimation and optimization Proposed Decomposition Adaptive volume scattering model Comparative experiments Conclusions This is the outline of my presentation. There are five parts. Firstly, I will briefly introduce the research background. Secondly, I will review the PolInSAR coherence estimation and optimization. In the third part, I will emphasize the proposed decomposition. The last two parts are experiments and conclusions.

3 ﹢ ﹢... = Introduction Polarimetric SAR (PolSAR)
Full polarimetric information Covariance matrix Model-based decomposition Better understanding the scattering mechanisms ALOS/PALSAR Pre-event After-event Optical Image Decomposition East Japan earthquake and tsunami Ps Pv Pd Color-code Recently, polarimetric SAR which can obtain full polarimetric information, becomes one of the mainstreams in microwave remote sensing. Under the reciprocity condition, all the information are included in the covariance matrix which are commonly used for data interpretation. Model-based decomposition is a powerful technique to better understand the scattering mechanism. The main principle is to decompose the covariance matrix into a summation of several basic scattering mechanisms, such as volume scattering, double- and single-bounce scattering. After decomposition, the RGB color-coding is used for each component. Here is an example for evaluation of the East Japan earthquake and tsunami in this March. Figures at left are pre-event, while at right are after-event. From the optical image, we can see most of the houses near the ocean were washed away and show relatively flat surface. And from the decomposition results, the single bounce scattering increases after the tsunami, and well match the observations. ﹢ ﹢... = Double Bounce Volume Scattering Single Bounce (PolSARpro tutorials)

4 Model-based decomposition
Freeman-Durden decomposition (1998) Double Bounce Volume Scattering Single Bounce Limitations Improvements Reflection symmetry assumption Negative power Scattering mechanism ambiguity Inadaptive Helix component Nonnegative eigenvalues In model-based decomposition, Freeman-Durden method is one of the earliest work. These developed basic models are widely used. However, this decomposition has its own limitations, such as producing negative power and causing scattering mechanism ambiguity and so on. During the last ten years, many groups keep working on this topic. Many excellent work have been achieved. Such as the proposal of nonnegative eigenvalues constraint, deorientation and so on. However, these limitations are not completely solved , especially for the scattering mechanism ambiguity and inadaptive models. Deorientation General volume model (A. Freeman, Y. Yamaguchi, W.M. Boerner, J. J. Van Zyl, J.S. Lee, Y. Q. Jin, M. Neumann, M. Arii and et al.)

5 Scattering mechanism ambiguity
General representation of the volume scattering model Pauli Image Skew-oriented building Decomposed volume scattering power For Freeman-Durden For Yamaguchi Here we try to investigate the scattering mechanism ambiguity in detail. This is the general representation of the volume scattering model. The decomposed volume scattering power is determined by the model coefficients and cross-polarization term C22. The lower limit is 3C22. And these are volume scattering power for Freeman-Durden and Yamaguchi methods. Here is a Pauli image. One skew-oriented building is selected, to examine the averaged backscattered power, shown in table I. We can see even after deorientation, the volume scattering power is still dominant for this building using Freeman-Durden and Yamaguchi decompositions. Furthermore, even the lower limit of the decomposed volume scattering shows dominant for this manmade structure. Therefore the scattering mechanism ambiguity occurs, from the classification viewpoint. Table I Averaged Backscattered Power (In dB) After Deorientation 19.48 13.52 16.75 12.79 18.81 18.53 17.56

6 Possible reasons and countermeasures
Double Bounce Volume Scattering Single Bounce Only Volume scattering Cross- Polarization Terrain slopes, oblique buildings Countermeasures Adaptive volume scattering model Indirect modification of double- and single-bounce models Balance the inputs and outputs The possible reasons are from the models themselves. Here we recall the current scattering models. We can see only the volume scattering contributes to the cross-polarization term. However, in real situation, terrain slopes or oblique buildings will rotate the polarization basis, and induce significant cross-polarization power. Therefore, not only the volume scattering model, but also the double- and single-bounce scattering models should be updated accordingly. This is the motivation of our work. Our idea is to develop an adaptive volume scattering model, and indirectly modify the double- and single-bounce models, meanwhile, balance the inputs and outputs, for model inversion. We will utilize both polarimetric and interferometric information to develop the new model. Next, we will review the PolInSAR coherence estimation and optimization. Utilization of both Polarimetric and Interferometric information!

7 Outline Introduction PolInSAR Coherence Proposed Decomposition
Current model-based decompositions Limitations PolInSAR Coherence Estimation and optimization Proposed Decomposition Adaptive volume scattering model Comparative experiments Conclusions

8 Polarimetric Dependence
PolInSAR coherence Polarimetric SAR interferometry (PolInSAR) Combination of PolSAR and InSAR Covariance matrix Coherence magnitude Optimization PolInSAR (T. Xiong) Polarimetric Dependence PolInSAR is a combination of Polarimetric SAR and interferometric SAR. All the information are in the 6*6 covariance matrix. PolInSAR coherence magnitude is an important indicator, and can be estimated by sample average. This is the definition. W1 and w2 can be interpreted as two polarimetric mechanisms, therefore coherence is polarimetric dependency. Optimization processing is developed to obtain the optimal coherence. From the solutions, three optimal coherence are available, called optimal1,2,3.

9 PolInSAR coherence Decorrelation sources Signal-to-noise decorrelation
Baseline decorrelation Processing decorrelation Temporal decorrelation Volume decorrelation … (K. Papathanassiou et al. ) NOTE: For manmade target For forest PolInSAR coherence: PolInSAR coherence has rich information, since it relates to many decorrelation sources, listed here. The decorrelation for different targets are quite different. For manmade target, which contains more permanent scatterers, is nearly unaffected by the temporal and volume decorrelation. Oppositely, forest and vegetation terrains commonly suffer from them and show lower coherence magnitude. Therefore, PolInSAR coherence is sensitive to different terrains, and can well discriminate the forest and manmade targets. Furthermore, based on PolInSAR coherence, the vertical profiles of forest can be reconstructed. Consequently, PolInSAR coherence also has a close relationship to forest structures. Based on these knowledge, potentially, the volume scattering can be modeled from it! Sensitive to diverse terrains Close relationship to forest structures Potentially, the volume scattering can be modeled from it!

10 PolInSAR Coherence Optical image Optimal 1 Here is an example showing the optimal PolInSAR coherence magnitudes. This is the corresponding optical image. Others are optimal coherence images. Clearly, different terrains show quite different coherence magnitudes. Meanwhile, for the same target, with different polarization combination, the coherence differs. Optimal 2 Optimal 3

11 PolInSAR Coherence Optimal 1 This is the histogram of these coherence magnitudes. For this dataset,Optimal3 shows more uniform distribution and can be more sensitive for diverse terrains. Optimal 2 Optimal 3

12 Outline Introduction PolInSAR Coherence Proposed Decomposition
Current model-based decompositions Limitations PolInSAR Coherence Estimation and optimization Proposed Decomposition Adaptive volume scattering model Comparative experiments Conclusions Next is the proposed decomposition using PolInSAR coherence.

13 Proposed decomposition
Adaptive volume scattering model (A. Freeman,2007) Modeled with PolInSAR coherence Where, is adjust to the spatial and temporal baseline parameters. Model compatibility Use Freeman-Durden model The structure of the proposed model is inspired by the Freeman’s volume scattering model. In this model, a parameter rou was introduced to fit for forest structures. However, due to this unknown parameter, the decomposition reduces to two components and loses the generality. Based on previous analysis, an adaptive volume model is proposed, and modeled with PolInSAR coherence magnitudes. And eta is adjust to the different spatial and temporal baseline parameters. To be compatible with previous model, if this condition meets, we will use Freeman-Durden volume model, like this. If

14 Model Parameters Two unknowns: Principles for the choice of are:
More uniform distribution More sensitive for diverse terrains In this proposed model, all the PolInSAR coherence magnitudes can be available from PolInSAR datasets. There are two unknowns: the selection of optimal coherence and a parameter eta. The principle for the choice of optimal coherence is: it should have more uniform distribution, thereby shows more sensitive for diverse terrains. Here are the histograms shown before. In this example, Optimal3 will be selected.

15 Indirect modification of double and single bounce scattering models!
Model Parameters NOTE: For Another unknown is eta. In order to constrain Cvol22 to be positive, the range of eta is 0 to 1. This figure illustrates the relationship among Cvol22, optimal coherence and parameter eta. With the increasing of optimal coherence and eta, the Cvol22 will be enhanced. Thereby, the total volume scattering power will be reduced. For the sake of maximum enhancement of Cvol22, eta can be set to 1,in a simple and empirical fashion. For eta equals 1, when optimal coherence magnitude is over 0.5, Cvol22 will be larger than Cvol11 , Cvol33. And this over portion can be interpreted as the contribution from double- and single-bounce scattering, contributing to the total cross-polarization power. Therefore, in this proposed volume model, it also includes the indirect modification of double- and single-bounce scattering mechanisms. Indirect modification of double and single bounce scattering models!

16 Decomposition Flowchart
Double & single bounce model Indirect modification Adaptive decomposition Pixel by pixel . This is the flowchart of the proposed decomposition. Inputs are PolInSAR covariance matrices. Then calculate the coherence. Based on this judgment, the used volume model can be determined. To avoid the negative power, we also adopt the Non-negative eigenvalues constraint. Following the previous theory, the double- and single-bounce dominated components can be decided. Therefore, the decomposed scattering components are available. For this proposed decomposition, The double- and single-bounce models are indirectly modified. The models are more adaptive for each pixel. Furthermore, all the three scattering mechanisms can contribute to the cross-polarization term adaptively and match the real situations. Volume scattering Double bounce Single bounce

17 Outline Introduction PolInSAR Coherence Proposed Decomposition
Current model-based decompositions Limitations PolInSAR Coherence Estimation and optimization Proposed Decomposition Adaptive volume scattering model Comparative experiments Conclusions

18 Experiment-I E-SAR PolInSAR data Test site: Oberpfaffenhofen, Germany
(PolSARpro tutorials) E-SAR PolInSAR data Test site: Oberpfaffenhofen, Germany L-band Data size : 1300×1200 Azimuth Range In this comparative experiment, L-band E-SAR PolInSAR datasets are used to demonstrate the performance of the proposed decomposition on discrimination of skew-oriented built-up regions. Left is the optical image. Middle is the Pauli image from master track. And the right is the PolInSAR coherence RGB image. Optical image Master track Pauli image HH-VV, HV, HH+VV PolInSAR coherence RGB image HH, HV, VV

19 Decomposition _ After deorientation
Ps Pv Pd Color-code Full scene Freeman-Durden Yamaguchi Proposed Forest region Freeman-Durden Yamaguchi Proposed Here are the decomposition results. Apparently, Freeman-Durden and Yamaguchi decompositions still suffer from the scattering mechanism ambiguity between the forests and skew-oriented built-up region even after deorientation. And two regions are selected for further comparison. This is the forest region, and all these methods show dominated volume scattering mechanism.

20 Skew-oriented built-up region
Freeman-Durden Yamaguchi Proposed This is the skew-oriented built-up region. Clearly, Freeman-Durden and Yamaguchi method still show volume scattering mechanism for these man-made structures. Only the proposed decomposition can successfully indentify them as double- or single-bounce scattering mechanism dominant. Here, I will go through these typical oblique buildings rapidly. First oblique building.

21 Experiment - II E-SAR PolInSAR data
BioSAR-2008 campaign L band Repeat-pass dataset Spatial Baseline: 30 m Temporal baseline: 110min Data size : 1496×840 Azimuth Range Now let’s move to the second experiment. L-band E-SAR PolInSAR datasets from BioSAR 2008 campaign are used, to illustrate the sensitivity of the proposed decomposition on discrimination of different forests. Left are the optical images. The forests in this specific shape were logged for products, after the BioSAR 2008. This is the Pauli image. And the right is the PolInSAR coherence RGB image. Logged after the BioSAR 2008 Mar. 2008 Jan. 2009 Oct Pauli Image Coherence RGB Image Optical Image HH-VV, HV, HH+VV 21 VV, HV, HH

22 Decomposition _ After deorientation
Ps Pv Pd Color-code From these decomposition results, we can see, only the proposed method can accurately indentify these logged forests. They were logged, since the maturity or other properties are quite different from the surroundings. And this is a good validation of the proposed decomposition. In addition, on the whole, visually the proposed decomposition shows better sensitivity for diverse forest terrains, and outperforms the other methods. Optical Image Freeman-Durden Yamaguchi Proposed More sensitive and better fit for diverse forest terrains! 22

23 Outline Introduction PolInSAR Coherence Proposed Decomposition
Current model-based decompositions Limitations PolInSAR Coherence Estimation and optimization Proposed Decomposition Adaptive volume scattering model Comparative experiments Conclusions

24 Conclusions Adaptive volume scattering model Adaptive decomposition
Using PolInSAR coherence Better fit for different terrains Indirect modification of double- and single-bounce scattering models Adaptive decomposition Fully usage of the information Successfully discriminate the skew-oriented buildings as manmade structures Overcome the scattering mechanism ambiguity Sensitive to diverse forest terrains Finally, I’d like to summarize this work. In this presentation, an adaptive volume scattering model, using PolInSAR coherence, is proposed. It is better fit for different terrains, and includes indirect modification of double- and single-bounce scattering models. Thereby, an adaptive decomposition is developed, using full polarimetric and interferometric information. From theoretical analysis and comparative experiments, the proposed decomposition can successfully discriminate the skew-oriented buildings as manmade structures. Therefore, it overcome the scattering mechanism ambiguity. Furthermore, it is more sensitive to diverse forest terrains and structures.

25 Thank you for your attention !

26 Limitation of current model
General representation of the volume scattering model Pauli Image Skew-oriented building Decomposed volume scattering power For Freeman-Durden For Yamaguchi Table I Averaged Backscattering Power (In dB) Before Deorientation 19.48 14.10 15.26 14.69 20.71 20.43 19.47 After Deorientation 13.52 16.75 12.79 18.81 18.53 17.56

27 PolInSAR Coherence Optical image HH-HH VV-VV HV-HV

28 PolInSAR Coherence HH-HH VV-VV HV-HV

29 Model Parameters Optimal 3 Coherence Optical Image

30 Skew-oriented built-up region
Scattering power Freeman-Durden Yamaguchi Proposed Table II Scattering Power Contribution (%) Method Built-up area Forest area Freeman-Durden 20 32 48 9 88 3 Yamaguchi 22 25 53 7 82 11 Proposed 29 8 63 13 81 6

31 Skew-oriented built-up region
Freeman-Durden Yamaguchi Proposed Second one.

32 Skew-oriented built-up region
Freeman-Durden Yamaguchi Proposed Third one.

33 Skew-oriented built-up region
Freeman-Durden Yamaguchi Proposed Last one.

34 PolInSAR Coherence Optimal 1 Optimal 2 Optimal 3

35 PolInSAR Coherence Optimal 1 Optimal 2 Optimal 3

36 Decomposition _ Volume scattering contribution
Optical Image Freeman-Durden Yamaguchi Proposed

37 ALOS/PALSAR datasets Optical Image Pauli Image Spatial baseline: 299m
Azimuth Range Optical Image Pauli Image Spatial baseline: 299m Temporal baseline: 46 days

38 PolInSAR coherence _ H-V
HH-HH VV-VV HV-HV

39 PolInSAR coherence _ Optimal

40 PolInSAR coherence _ Histogram

41 Decomposition _ After deorientation
Ps Pv Pd Color-code Freeman-Durden Yamaguchi Proposed

42 Built-up region - I Ps Pv Pd Color-code Optical image Freeman-Durden
Yamaguchi Proposed

43 Built-up region - II Ps Pv Pd Color-code Optical image Freeman-Durden
Yamaguchi Proposed

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