1. Random Forest Feature Selection for SAR-ATRBy
Pouya Bolourchi
Masoud Moradi
Hasan Demirel
Sener Uysal
March 2018

2. Outline Synthetic Aperture Radar Objectives Contributions
Moment methods
Proposed method
Experimental results
Conclusions
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

3. What is SAR?
SAR refers to a technique used to synthesize a long antenna by combining signals (echoes) received by the radar as it moves along its flight track.
Aperture means the opening used to collect the reflected energy that is used to form an image. In the case of radar imaging this is the antenna.
An antenna, mounted on a spacecraft.
The signal processing uses magnitude and phase of the received signals over successive pulses.
After a given number of cycles, the stored data is recombined to form a finer resolution.
Depression angle refers to the angle between the line of sight from the radar to the center of illuminated object and horizontal plane
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

4. Why SAR are introduced? They have a very high resolution
They have the capability to work independently from any weather condition
They have the capability to work independently from solar illumination
It is expensive to place very large antennas in space, therefore we simulate an extremely big antenna
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

5. Applications of SAR Fire detection Flood detection
Earthquake detection
Ship detection
Wave forecasting
Agricultural industry
Homeland security applications
Military surveillance systems
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

6. Objectives
Adopt a method that is robust to noise with minimum information redundancy as well as being scale, translation and rotation invariant.
ü Apply Moment Methods
Noisy background:
ü Isolate the ROI by segmentation after background removal.
Dimensionality reduction with higher class separability
ü Apply feature ranking approaches as a feature selection method in SAR ATR.
Improvement in the recognition performance
ü Apply data fusion
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

7. Contributions
To utilize different segmented parts (Area, Boundary, Texture), Combining different segmentation parts with different regions (Target, Shadow)
Feature ranking based on Random Forest
Data Fusion (Feature Fusion and ensemble of classifiers)
After feature selection, the most discriminant features are grouped to represent an image, which in turn increases the classification performance.
Finally, the performance of the proposed method is calculated by majority voting based on all output labels corresponding to each classifier method.
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

8. Proposed Method
UKSim-AMSS 20th International Conference on Modelling & Simulation
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9. Infantry-fighting vehicle
MSTAR data
Type/ Class
Samples
17°Angle
15°Angle
Number of Samples
BTR70
233
196
429
BMP2
698
587
1285
T72
691
582
1274
Total
1622
1365
2987
3-class recognition
MSTAR DATA
10-class recognition
Type
Description
BMP2
Infantry-fighting vehicle
BTR70
Armored car
T72
Tank
BTR60
2S1
Cannon
BRDM2
Truck D7
Bulldozer
T62
ZIL
ZSU
Type/ Class
Samples
17°Angle
15°Angle
BMP2
698
587
BTR70
233
582
T72
691
196
BTR60
256
195
2S1
299
274
BRDM2
298 D7
T62
273
ZIL131
ZSU-23-4
BMP BTR T BTR S1
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018
BRDM D T ZIL ZSU

10. Histogram equalization
Segmentation
Histogram equalization
Average Filter
Threshold
Aim: To extract ROIs
ROIs include:
Target: 1- Texture of target
Shadow: 2- Texture of shadow (SA)
Combined Target-Shadow:
3- Texture of target and shadow
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

11. Feature Extraction
Feature extraction techniques are utilized to extract low-dimensional representations from high-dimensional SAR images
Moment -based descriptors are used as an effective region-based shape descriptor
Why Moments are Introduced?
They have fast numerical implementation
They avoid a high degree of information redundancy
They are scale, translation and rotational invariant
Feature extraction algorithms extract unique target information from each image
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

12. Moments in General Moments are scalar quantities
They used to capture dominant features of an Image
They are widely used in many applications such as image enhancement, object recognition, edge detection, texture analysis and image reconstruction.
They can be defined as the projection of a function onto a polynomial basis. For a digital SAR image f (x,y) general form of moments can be written as:
𝑀 𝑝,𝑞 = 𝑅 𝑝𝑞 𝜌 𝑒 −𝑖𝑞𝜃 𝑓 𝜌,𝜃
where p and q are called the order and repetition of the moment respectively with p,q=0,1,… .
𝑅 𝑝𝑞 𝜌 is called radial part of polynomial and 𝑒 −𝑖𝑞𝜃 indicates angular part of polynomials.
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

13. Radial part of Polynomial Basis Function
Moments in polar
Method
Radial part of Polynomial Basis Function
Zernike Moments (ZM)
𝑅 𝑝𝑞 𝜌 = 𝑠=0 ( 𝑝−|𝑞| 2 ) −1 𝑠 𝑝−𝑠 ! 𝜌 𝑝−2𝑠 𝑠! 𝑝+ 𝑞 2 −𝑠 ! 𝑝− 𝑞 2 −𝑠 !
Pseudo Zernike Moments (PZM)
𝑅 𝑝𝑞 𝜌 = 𝑠=0 𝑝−|𝑞| −1 𝑠 2𝑝+1−𝑠 ! 𝜌 𝑝−𝑠 𝑠! 𝑝− 𝑞 −𝑠 ! 𝑝+ 𝑞 +1−𝑠 !
Radial Chebyshev Moments (RCM)
𝑅 𝑝 ρ = 𝑝! 𝜌(𝑝,𝑁) 𝑠=0 𝑝 ( −1) 𝑝−𝑠 𝑁−1−𝑠 𝑝−𝑠 𝑝+𝑠 𝑝 𝜌 𝑠
 where𝜌 𝑝,𝑁 = 𝑁 1− 1 𝑁 − 𝑁 2 …(1− 𝑝 2 𝑁 2 ) 2𝑝+1
PZM: The lower order contains more dominant information.
RCM: overcome numerical errors and reduce the computational complexity due to normalization
𝑓 (𝑟,𝜃) 𝑓 (𝑟,𝜃) =𝑓 𝑟𝑐𝑜𝑠𝜃,𝑟𝑠𝑖𝑛𝜃 , 𝑅 𝑝𝑞
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

14. Rank Important Features
All Features
DT1
DT2
DTn-1
DTn
Important Features of DT1
Important Features of DT2
Important Features of DTn-1
Important Features of DTn
Rank Important Features

15. Feature Selection
Main goal of this paper has dimensionality reduction by RF
In RF has n decision trees
In each tree select the important features
At the end in the test data we rank the features by RF score
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

16. Feature Selection + 58 Features selected from RCM
48 Features selected from PZM
Feature Fusion +
47 Features selected from ZM
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

17. Performance Metrics
UKSim-AMSS 20th International Conference on Modelling & Simulation
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18. Results
Performance Metrics of different moment methods followed by different classifiers
Method
ACC (%)
TPR (%)
TNR (%)
ZM+SVM
96.9
96.8
97.2
ZM+k-NN
92.9
93.0
94.2
PZM+SVM
93.1
91.7
PZM+k-NN
85.1
82.2
82.9
RCM+SVM
91.5
RCM+k-NN
85.2
82.6
83.3
ZM+RF+SVM
96.1
95.9
ZM+RF+k-NN
92.1
92.6
93.3
PZM+RF+SVM
96.4
PZM+RF+k-NN
94.4
94.8
RCM+RF+SVM
91.1
91.4
RCM+RF+k-NN
84.3
81.1
81.7
ZM+PZM+RCM+RF+SVM
98.6
98.4
98.5
ZM+PZM+RCM+RF+k-NN
95.7
95.6
96.5
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

19. Confusion Matrix of proposed method
UKSim-AMSS 20th International Conference on Modelling & Simulation
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20. ROCs of ZM, PZM and RCM for SVM classifier
UKSim-AMSS 20th International Conference on Modelling & Simulation
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21. ROCs of ZM, PZM and RCM for k-NN classifier.
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

22. Comparison of proposed method with other techniques
Accuracy (%)
LDA [1]
87.4
ZM [2]
89.4
Template Matching [3]
90.4
PCA+LDA+ICA [4]
90.6
MINACE [5]
PCA [1]
93.3
Seven EFS Coefficient [5]
93.5
QP normalized Image [6]
94.1
RHFM+LBP+HWT+RT+PCA+SVM [7]
98.1
Proposed method
98.6
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

23. Conclusions:
Proposed method is superior to traditional methods in the case of target detection and reducing false alarms
Improving in the accuracy achieved by Feature Level Fusion
Considering the shadow part and defining different ROIs helps to improve the accuracy.
Random Forest is effectively used to reduce feature dimensionality and improve the classification.
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

24. Publications
Bolourchi, P., Demirel, H., & Uysal, S. (2018).“Entropy score based feature selection for moment based SAR image classification”, IET Electronics Letter
Bolourchi, P., Demirel, H., & Uysal, S. (2017). Target recognition in SAR images using radial Chebyshev moments. Signal, Image and Video Processing. vol. 11, no.6, pp. 1033–1040
Bolourchi, P., Moradi, M., Demirel, H., & Uysal, S. (2017, April). Feature Fusion for Classification Enhancement of Ground Vehicle SAR Images. UKSim-AMSS 19th International Conference on Modelling & Simulation. European (pp ). IEEE.
Bolourchi, P., Demirel, H., & Uysal, S. (2016, November). Continuous Moment-Based Features for Classification of Ground Vehicle SAR Images. In Modelling Symposium (EMS), 2016, European (pp ). IEEE.
Bolourchi, P., & Uysal, S. (2013, June). Forest fire detection in wireless sensor network using fuzzy logic. In Computational Intelligence, Communication Systems and Networks (CICSyN), 2013 Fifth International Conference on (pp ). IEEE.
Pouya Bolourchi, Hasan Demirel and Sener Uysal “Improved SAR Target Recognition using Fisher Criterion and Data Fusion”,  Journal International Journal of Pattern Recognition and Artificial Intelligence, Submitted. 
Pouya Bolourchi, Masoud Moradi, Hasan Demirel, Sener Uysal “Ensembles of classifiers for improved SAR image recognition using Pseudo Zernike moments”,IET Computer Vision, Submitted.
UKSim-AMSS 20th International Conference on Modelling & Simulation
11/10/2018

25. References
[1] B. Wang, Y. Huang, J. Yang and J Wu, “A feature extraction method for synthetic aperture radar (SAR) automatic target recognition based on maximum interclass distance,” Science China Technological Sciences., vol. 54, no. 9, pp , 2011.
[2] P. Bolourchi, H. Demirel, H and S. Uysal, “Continuous Moment-Based Features for Classification of Ground Vehicle SAR Images,” In Modelling Symposium (EMS)., 2016, European, pp , 2016, November.
[3] Q. Zhao and J.C. Principe, “Support vector machines for SAR automatic target recognition,” IEEE Transactions on Aerospace and Electronic Systems., vol. 37, no. 2, pp , 2001.
[4] X. Yuan, T. Tang, D. Xiang, Y. Li and Y. Su, “Target recognition in SAR imagery based on local gradient ratio pattern,” International journal of remote sensing., vol. 35, no. 3, pp , 2014.
[5] R. Patnaik and D. Casasent, “SAR classification and confuser and clutter rejection tests on MSTAR ten-class data using Minace filters,” In Optical Pattern Recognition XVIII  International Society for Optics and Photonics. vol , p , April, 2007.
[6] P. Bolourchi, M. Moradi, H. Demirel and S. Uysal, “Feature Fusion for Classification Enhancement of Ground Vehicle SAR Images,” UKSim-AMSS 19th International Conference on Modelling & Simulation., European, pp , April
[7] M. Amoon and G.A. Rezai-Rad, “Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features,” IET Computer Vision., vol. 8, no.2, pp , 2013.
UKSim-AMSS 20th International Conference on Modelling & Simulation
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26. Thank You
UKSim-AMSS 20th International Conference on Modelling & Simulation
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