Random Forest Feature Selection for SAR-ATR By Pouya Bolourchi Masoud Moradi Hasan Demirel Sener Uysal March 2018

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

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

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

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

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

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

Proposed Method UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

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 BMP2 BTR70 T72 BTR60 2S1 UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018 BRDM2 D7 T62 ZIL ZSU

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

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

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

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 2 1− 2 2 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

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

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

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

Performance Metrics UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

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

Confusion Matrix of proposed method UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

ROCs of ZM, PZM and RCM for SVM classifier UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

ROCs of ZM, PZM and RCM for k-NN classifier. UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

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

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

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. 111-115). 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. 53-57). 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. 83-87). 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

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. 2520-2524, 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. 53-57, 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. 643-654, 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. 857-870, 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. 6574, p. 657402, 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. 111- 115, April. 2017. [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. 77-85, 2013. UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018

Thank You UKSim-AMSS 20th International Conference on Modelling & Simulation 11/10/2018