Abstract
In the first part of this paper several aspects of the SAR imaging are presented. Firstly, the mathematical theory and methodology for generating SAR synthetic backscattered data are developed. The simulated target is a ship, which is located on the sea surface. A two-dimensional and a three-dimensional target (ship) implementations are included in the simulations. Both cases of airborne and spaceborne SAR are simulated. Furthermore, the case of varying target scattering intensity is presented. In addition an application of an autofocusing algorithm, previously developed by the authors for the case of Inverse Synthetic Aperture Radar (ISAR) and Synthetic Aperture Radar (SAR) geometry for simulated data, is presented here for the case of real-field radar data, provided to us by SET 163 Working Group. This algorithm is named “CPI-split-algorithm”, where CPI stands for “Coherent Processing Interval”. Numerical results presented in this paper show the effectiveness of the proposed autofocusing algorithm for SAR image enhancement. In the second part of this paper the Modified Fractal Signature (MFS) method is presented. This method uses the “blanket” technique to provide useful information for SAR image classification. It is based on the calculation of the volume of a “blanket”, corresponding to the image to be classified, and then on the calculation of the corresponding fractal signature (MFS) of the image. We present here some results concerning the application of MFS method to the classification of SAR images. The MFS method is applied both in simulated data (comparison of a focused and an unfocused image) and in real-field data provided to us by SET 163 Working Group (comparison of a “town” area, “suburban” area and “sea” area). In these results it is clearly seen that the focusing of the SAR radar image clearly correlates with the value of MFS signature for the simulated data, and that the type of area can be distinguished by the value of MFS signature for the real data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D. Wehner, High-Resolution Radar,Artech House, 2nd edition, London, 1995.
V. Chen, H. Ling, Time-Frequency Transforms for Radar Imaging and Signal Analysis,Artech House,London, 2002.
E. Kallitsis, A. Karakasiliotis, G. Boultadakis, P. Frangos, “A Fully Automatic Autofocusing Algorithm for Post-processing ISAR Imaging based on Image Entropy Minimization”, Electronics and Electrical Engineering Journal, No. 4 (110), pp. 125–130, April 2011.
A. Lazarov, C. Minchev, “ISAR Signal Modeling and Image Reconstruction with Entropy Minimization Autofocusing”, Proc. DASC, Portland, USA, Oct 2006, pp. 3E5-1–3E5-11.
J. Li, R. Wu, V. Chen, “Robust autofocus algorithm for ISAR imaging of moving”, IEEE Trans. Aerosp. Electron. Syst., Vol. 37, No 3, pp. 1056–1069, 2001.
D. Pastina, A. Farina, J. Gunning, P. Lombardo, “Two-dimensional super resolution spectral analysis applied to SAR images”, IEE Proc., Radar Sonar Navig., Vol. 145, No 5, pp. 281–290, 1998.
A. Malamou, A. Karakasiliotis, E. Kallitsis, G. Boultadakis and P. Frangos, “An autofocusing algorithm for post-processing of synthetic aperture radar (SAR) images based on image entropy minimization”, CEMA’12 International Conference, National Technical University of Athens (NTUA), Athens, Greece, 8-10/11/2012, pp. 53–56.
A. Malamou, A. Karakasiliotis, E. Kallitsis, G. Boultadakis, P. Frangos, ‘Application of a Fully Automatic Autofocusing Algorithm for Post Processing of Synthetic Aperture Radar Images based on Image Entropy Minimization’, ‘Electronics and Electrical Engineering’ Journal, Vol. 19, No.6, June 2013, pp. 95–98.
Y. Tang, H. Ma, D. Xi, X. Mao, C. Suen, ‘Modified Fractal Signature (MFS): A New Approach to Document Analysis for Automatic Knowledge Acquisition’, IEEE Transactions on knowledge and data engineering, Vol. 9, No. 5, Sept. – Oct. 1997, pp. 747–762.
N.B. Ampilova, E.Y. Gurevich, I.P. Soloviev, ‘Application of modified fractal signature and Regny spectrum methods to the analysis of biomedical preparation images, CEMA’11 Conference, Sofia, Bulgaria, October 2011, pp. 96–100.
K. J. Falconer, ‘Fractal Geometry: Mathematical Foundations and Applications’, J. Wiley and Sons, 1990.
D. Jaggard, A. Jaggard and P. Frangos, ‘Fractal electrodynamics: surfaces and superlattices’, in ‘Frontiers in Electromagnetics’, IEEE Press, Edited by D. Werner and R. Mittra, 2000.
C. Pandis, A. Malamou, P. Stefaneas and P. Frangos, ‘Applying the modified fractal signature method to image classification: some preliminary results for ISAR radar images’, CEMA’12 International Conference, National Technical University of Athens (NTUA), Athens, Greece, 8-10/11/2012.
Acknowledgements
The authors (AM, AK, EK, PF) would like to acknowledge SET 163 Working Group, and its Chairman Dr. Luc Vignaud (ONERA, France) in particular, for providing us with the real-field Synthetic Aperture Radar (SAR) data, which were used to reconstruct the SAR images of moving ship shown in Figs. 8 and 9 above, as well as the SAR image of “Oslo fjord” shown in Fig. 13. In particular, the radar data concerning the moving ship of Figs. 8 and 9 were provided to SET 111 and SET 163 Working Groups by Dr. William Miceli (ONR) and their origin is from a radar developed by “Radar Branch of the Naval Command Control and Ocean Surveillance Center”, Research Development Test and Evaluation Division (NRaD), San Diego, CA, USA. Furthermore, the “Oslo fjord” image of Fig. 13 was produced by DLR, Germany (spaceborne image). To all the above institutes and involved scientists we express our sincere thanks for providing these real-field radar data to us, in the framework of SET 163 Working Group.
This research has been co-financed by the European Union (European Social Fund) and the Greek National Funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: THALIS.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Malamou, A., Pandis, C., Karakasiliotis, A., Stefaneas, P., Kallitsis, E., Frangos, P. (2014). SAR Imaging: An Autofocusing Method for Improving Image Quality and MFS Image Classification Technique. In: Daras, N. (eds) Applications of Mathematics and Informatics in Science and Engineering. Springer Optimization and Its Applications, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-319-04720-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-04720-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04719-5
Online ISBN: 978-3-319-04720-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)