Clutter removal in millimeter wave GB-SAR images using OTSU’s thresholding method

Year 2022, Volume: 7 Issue: 1, 43 - 48, 15.02.2022

Enes Yiğit , Şevket Demirci , Caner Özdemir

https://doi.org/10.26833/ijeg.867467

Cited By: 5

Abstract

In this study, a filtering method based on the threshold value of normalized synthetic aperture radar (SAR) data is proposed to eliminate clutter in millimeter wave ground based synthetic aperture radar (GB-SAR) images. In the proposed method, first, stepped frequency continuous wave SAR data are reconstructed by using the back-projection algorithm and focused complex SAR data are obtained. Then, the amplitude values of the complex SAR data are normalized and the best threshold values to distinguish the target from clutter is determined by the OTSU’s thresholding method. Next, a filter mask is created that cancels all data below the computed threshold values. The mask matrix is finally multiplied with the resulted GB-SAR data to eliminate all clutter from the image. With the proposed technique, the best threshold value is determined automatically by directly processing the raw data without converting the SAR data into any RGB images. The proposed technique is validated through real GB-SAR experiments that were carried out in the frequency band of 78-81 GHz. In the experiments, challenging GB-SAR data are obtained using high cluttered background materials, and very successful filtering operations are performed with the proposed technique.

Keywords

Synthetic Aperture Radar, OTSU, Back projection Algorithm, Clutter Cancellation

Thanks

The authors would like to thank Mr. Atilla Unal and Mr. Mustafa Tekbas for their assistance during the experiments. The authors also thank the ILHT for providing measurement facilities

References

• Beasley P, Binns G, Hodges R & Badley R (2004). A Millimetre Wave Radar for Airport Runway Debris Detection. First Europen Radar Conference, Amsterdam, Nerherlands, 261-264.
• Conte E, Longo M, Lops M & Ullo S L (1991). Radar detection of signals with unknown parameters in K-distributed clutter. IEE Proc F Radar Signal Process 138, 131–138.
• Demirci S, Cetinkaya H, Yigit E, Ozdemir C & Vertiy A A (2012). A Study on Millimeter-Wave Imaging of Concealed Objects: Application Using Back-Projection Algorithm. Progress In Electromagnetics Research, 128, 457-477.
• Demirci S, Ozdemir C, Akdagli A & Yigit E (2008). Clutter Reduction In Synthetic Aperture Radar Images With Statistical Modeling: An Application To Mstar Data. Microwave And Optical Technology Letters.50 (6).
• Engin E, Çiftçioğlu B, Özcan M & Tekin İ (2007). High Resolution Ultrawideband Wall Penetrating Radar. Microwave and optical technology letters, 49(2), 320-325.
• Freitas C C, Frery A C & Correia A (2005). The polarimetric G distribution for SAR data analysis. Environmetrics 16, 13–31.
• Gomez-Dans J L, Quegan S & Bennett J C (2006). Indoor C-Band Polarimetric Interferometry Observations of a Mature Wheat Canopy. IEEE transactions on geoscience and remote sensing, 44(4), 768-777.
• Işıker H & Özdemir C (2019). A Multi-Thresholding Method Based on Otsu’s Algorithm for the Detection of Concealed Threats in Passive Millimeter-Wave Images. Frequenz, 73, issue 5-6, 179-187.
• Işıker H, Özdemir C, Unal İ (2015). Millimeter-Wave Band Radiometric Imaging Experiments for the Detection of Concealed Objects. IEEE Radar Conference, Johannesburg, South Africa, 27-30 October.
• Işıker H, Ünal İ, Tekbaş M, Özdemir C (2018). An Auto-Classification Procedure for Concealed Weapon Detection in Millimeter-Wave Radiometric Imaging Systems. Microwave Opt. Tech. Letters, 60(3), 583–594.
• Jaeger I, Zhang L, Stiens J, Sahli H & Vounckx R (2007). Millimeter Wave Inspection of Concealed Objects. Microwave and optical technology letters, 49(11), 2733-2737.
• Khoukhi H, Filali Y, Yahyaouy A, Sabri M A, Aarab A (2019). A hardware Implementation of OTSU Thresholding Method for Skin Cancer Image Segmentation. 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS),
• Lu J & Hu R (2012). A new image segmentation method based on Otsu method and ant colony algorithm," 2012 International Conference on Computer Science and Information Processing (CSIP), Xi'an, China, 2012, pp. 767-769, doi: 10.1109/CSIP.2012.6308966.
• Otsu N (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66
• Ozkaya U (2020). Automatic Target Recognition (ATR) from SAR Imaginary by Using Machine Learning Techniques, European Journal of Science and Technology, 165-169
• Ozkaya U, Seyfi L (2018). Deep dictionary learning application in GPR B-scan images. Signal, Image and Video Processing, 12, 1567–1575
• Pieraccini M, Luzi G, Mecatti D, Noferini L & Atzeni C (2006). Ground-Based Sar for Short and Long Term Monitoring of Unstable Slopes. 3rd Europen Radar Conference, Manchester, United Kindgom, 92-95.
• Sabanci K, Yigit E, Toktas A, Kayabasi A (2018). A Hue-domain filtering technique for enhancing spatial sampled compressed sensing-based SAR images. IET Radar, Sonar&Navigation, 13(3), 357-367.
• Toktas A, Yigit E, Sabanci K, Kayabasi A. (2017). CFAR based morphological filter design to remove clutter from GB-SAR images: An application to real data. Microw Opt Technol Lett. 59, 2685 –2692.
• Yigit E, Demirci S, Ozdemir C, Tekbas M (2013). Short-range ground-based synthetic aperture radar imaging: performance comparison between frequency-wavenumber migration and back-projection algorithms. J. Appl. Rem. Sens. 7(1).
• Yigit E, Demirci S, Unal A, Ozdemir C & Vertiy A (2012). Millimeter-Wave Ground-Based Synthetic Aperture Radar Imaging for Foreign Object Debris Detection: Experimental Studies at Short Ranges. Journal of Infrared, Millimeter, and Terahertz Waves, 33(12), 1227-1238.
• Yigit E, Ozkaya U, Ozturk S (2020). Enhancement of Near Field GB-SAR Image Quality Using Beamwidth Filter. European Journal of Science and Technology Special Issue, 480-487.