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Artificial Intelligence based Image Classification of Synthetic Aperture Radar

G. B. Giri*, P. M. Menghal**

*-** Department of Radar & Control System, Faculty of Electronics, Military College of Electronics & Mechanical Engineering, Secunderabad, Telangana, India.

Periodicity:October - December'2020
DOI : https://doi.org/10.26634/jip.7.4.17341

Abstract

Synthetic Aperture Radar (SAR) imaging has been an exciting field owing to its wide application in military/non military fields. This paper is aimed at investigating effects of using different types of signals and emission parameters on the accuracy and resolution of simulated SAR image and performing simulation on a reconstructed SAR image of an fully polarimetric SAR for identifying different land features. The speckles in the image are removed using the Lee filter and then the image is decomposed using Pauli decomposition method. After feature extraction, neural network techniques are used for training, validating and testing the network. The final image classified in RGB, representing features like urban, vegetation and water has been achieved. The percentage classification of each feature pixels in the image has been presented using confusion matrix.

Keywords

Synthetic Aperature Radar (SAR), Confusion Matrix, Lee Filter Method, Pauli Decomposition Method.

How to Cite this Article?

Giri, G. B., and Menghal, P. M. (2020). Artificial Intelligence based Image Classification of Synthetic Aperture Radar. i-manager's Journal on Image Processing, 7(4), 1-9. https://doi.org/10.26634/jip.7.4.17341

References

[1]. Berens, P. (2006). Introduction to synthetic aperture radar (SAR). Brussels, Belgium: North Atlantic Treaty Organisation.

[2]. Chan, Y. K., & Koo, V. C. (2008). An introduction to synthetic aperture radar (SAR). Progress in Electro magnetics Research, 2, 27-60.

[3]. Curlander, J. C., & McDonough, R. N. (1991). Synthetic aperture radar (Vol. 11). New York: Wiley.

[4]. Ersahin, K., Cumming, I., & Yedlin, M. (2006, July). Classification of polarimetric SAR data using spectral graph partitioning. In 2006, IEEE International Symposium on Geoscience and Remote Sensing (pp. 1756-1759). IEEE.

[5]. Fason, B.J. (2013). Modeling and simulation of synthetic aperture radars in MATLAB. (Postgraduate thesis). Naval Postgraduate School Monterey, California. Retrieved https://core.ac.uk/download/pdf/36727277.pdf

[6]. Gong, M., Zhou, Z., & Ma, J. (2011). Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Transactions on Image Processing, 21(4), 2141-2151.

[7]. Hongyuan, Z., Yunhua, Z., & Jingshan, J. (2000, August). Synthetic aperture radar simulation system based on Matlab. In 2000, 5^th International Symposium on Antennas, Propagation, and EM Theory (pp. 130-133). IEEE.

[8]. Kent, S., Kartal, M., Kasapoglu, N. G., & Kargin, S. (2007, June). Synthetic aperture radar raw data simulation rd for microwave remote sensing applications. In 2007, 3 International Conference on Recent Advances in Space Technologies (pp. 389-392). IEEE.

[9]. Lopez, J. X., Garza, G., & Qiao, Z. (2010, April). Synthetic aperture radar imaging in the cross-range. In Signal and Data Processing of Small Targets 2010 (Vol. 7698, p. 76981C). International Society for Optics and Photonics.

[10]. Rahmanizadeh, A., & Amini, J. (2017). An integrated method for simulation of synthetic aperture radar (SAR) raw data in moving target detection. Remote Sensing, 9(10), 1009-1021. https://doi.org/10.3390/rs9101009

[11]. Schlutz, M. (2009). Synthetic aperture radar imaging simulated in MATLAB (Master Thesis). California Polytechnic State University, San Luis Obispo. https://doi.org/10.15368/ theses.2009.106

[12]. Skolnik, M. I. (2008). Radar handbook. McGraw-Hill Education.

[13]. Soumekh, M. (1999). Synthetic aperture radar signal processing (Vol. 7). New York: Wiley.

[14]. Wang, X., Cao, Z., Ding, Y., & Feng, J. (2017). Composite kernel method for PolSAR image classification based on polarimetric-spatial information. Applied Sciences, 7(6), 612-618.

[15]. Wei, G. (2009). A new classifier for polarimetric SAR images. Progress in Electro-magnetics Research, 94, 83–104.

[16]. Yang, S., Wang, M., Long, H., & Liu, Z. (2016). Sparse robust filters for scene classification of synthetic aperture radar (SAR) images. Neuro-computing, 184, 91-98.

[17]. Zhang, Y., Wu, L., Neggaz, N., Wang, S., & Wei, G. (2009). Remote-sensing image classification based on an improved probabilistic neural network. Sensors, 9(9), 7516-7539.

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Artificial Intelligence based Image Classification of Synthetic Aperture Radar

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