Procedia Environmental Sciences 10 ( 2011 ) 1017 – 1022

doi: 10.1016/j.proenv.2011.09.163

Available online at www.sciencedirect.com

2011 3rd International Conference on Environmental Science and Information Application Technology

Synthesis of Interferogram Based on DEM of Dilijan in Caucasus region, Armenia

Full Prof. Andon Lazarova, Assist.Prof. Dimitar Mincheva aBurgas Free University, 62 San Stefano Str., Burgas, 8000, Bulgaria

Abstract

In this paper an algorithm for synthesis of a Synthetic Aperture Radar (SAR) interferogram is suggested. Two Single Look Complex (SLC) images are derived which are based on the Digital Elevation Map (DEM) of Dilijan region in Armenia. Using such kind of data SLC images of the SAR interferometric pair are produced by measurements of distances from two SAR satellites to each pixel of the area of interest at the moment of SAR image reconstruction. The unwrapped phases can be calculated through the interferometric pair and then the wrapped phases can be disclosed. As a result the interferometric fringes of the surface of observation can be generated. A numerical experiment is carried out in MATLAB environment and the results are provided. The algorithms suggested in this study can be exploited for the purposes of simulating, modeling and investigating Interfrometric SAR (InSAR) processing steps, and generating synthetic interferograms of particular areas on the Earth.

Keywords: InSAR; SAR interferometry; SAR interferogram synthesis; SAR remote sensing.

1. Introduction

The space based microwave instruments like SAR are advanced tools for monitoring the Earth's surface [1-7]. They are built on the principle of probing the Earth's surface and the objects on it with high informative electromagnetic pulses and registration of the backscattered radiation from them. The resulting images are depicted in two coordinates: slant range, or time delay, and azimuth, or cross range. The high resolution along the first coordinate is realized by using the bandwidth emitted pulses and along the second coordinate - by coherent summation of the reflected signals during the process of observation. A particular application of SAR systems is in the field of SAR interferometry to extract three-dimensional images of the observed objects. The SAR interferometry uses amplitude and phase information of two

2011 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011)

1878-0296 © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.

© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.

Open access under CC BY-NC-ND license.

Open access under CC BY-NC-ND license.

1018 Andon Lazarov and Dimitar Minchev / Procedia Environmental Sciences 10 ( 2011 ) 1017 – 1022

complex images of the observed surface obtained by SAR systems [8-11]. One of the perspective methods of studying complex SAR images and their application in SAR interferometry is the mathematical modeling of the ground surface relief. There are different interpretations of this problem. The simulation model of the surface topography on the basis of fractal Braun movement is presented in [12]. A cosine function is applied [13] for the aims of modeling the intensity of the backscattering radiation from the ground surface. The main goal of this work is to create a synthesized SAR interferogram based on DEM of Dilijan region.

2. Geographic description of the research area

Dilijan is a town in Caucasus region, Armenia, located at the geographical coordinates: 40° 44' 27" north and 44° 51' 47" east longitude. A geo tiff file, containing geographic information about the area of interest, is used. Through programming in MATLAB environment, master and slave complex images (Single Look Complex Images) are synthesized. From the so-prepared interferometer pair of two SLC images a SAR interferogram of unwrapped phases is generated. Then a phase wrapping algorithm is applied to illustrate the results of the SAR processing steps.

.

Fig.1. Location of Dilijan, Caucasus region, Armenia

Fig.1 shows the map of Armenia and the location of Dilijan region.

3. Algorithm and programming

The source data file is a Digital Elevation Map (DEM) of the Dilijan region and is used for processing purposes. In Fig.2 a geo-tiff image of Dilijan region is shown. The program code fragment developed by the authors provides an option to select a particular area of interest, the stage that will be processed and

1019 Andon Lazarov and Dimitar Minchev / Procedia Environmental Sciences 10 ( 2011 ) 1017 – 1022

visualized by the interferometric technique. The geo-tiff data file contains information about the heights, as it can be seen on the scale on the right of the figure.

Fig.2. Selecting the area of interest

Two synthetic aperture radars are placed on two tandem working satellite systems whose coordinates at the time instant of imaging are as follows:

SAR -I: XS1 = 0 m, YS1 = 300,3.103 m, ZS1 = 3.105 m. SAR -II: XS2 = 0 m, YS2 = 300.103 m, ZS2 = 3.105 m. The displacement of the satellites defines a baseline of 300 m between two SAR antennas. The SAR

wavelength is 5.10-2 m. The distances from the satellites to each pixel on the region of interest are calculated by square root of

the sum of square differences between the coordinates of the satellites and each pixel on the surface, as follows:

21

21

211 ZZYYXXR SSS (1)

22

22

222 ZZYYXXR SSS , (2)

where R1 is the two dimensional matrix of the range distances from the first satellite to each pixel on the surface, R2 is the two dimensional matrix of the range distances from the second satellite to each pixel on the surface, i.e. the distances R1 and R2 are functions of X and Y coordinates of each pixel of the area of interest.

Fig. 3. shows the calculated range distances from SAR-I and SAR-II to each pixel on the surface. On the left are the distances to the first satellite and on the right are the distances to the second one.

Fig.3. (a) Distances from the first satellite to the pixels on the ground; (b) Distances from the second satellite to the pixels on the ground, represented in pseudo color map.

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The phase differences are calculated by multiplication of the subtraction of range distances, R1 and R2

by four times divided by the SAR wavelength, which as a result produces a wrapped interferometric phase of each pixel, i.e.

)(412 RRPunwrapped , (3)

where Punwrapped is the unwrapped phase, is the wavelength, R1 and R2 are the distances for the each

one of the satellites and for every pixel on the surface. Then the wrapped phase can be calculated by extracting the defined integer number from the

unwrapped phase. For this purpose additional variable K for definition of integer number is introduced. The maximum value of K multiplied by 2 has to be extracted from the unwrapped phase calculated by expression (3). Therefore, the final result, a wrapped phase will be in a proper interval from minus to plus , i.e.

KPP unwrappedwrapped max2 , (4)

122 RRK , (5)

Fig.4. (a) Unwrapped phase; (b) wrapped phase.

1021 Andon Lazarov and Dimitar Minchev / Procedia Environmental Sciences 10 ( 2011 ) 1017 – 1022

On Fig. 4 the gray scale maps of the computed phases are shown: (a) unwrapped phase, (b) wrapped phase.

Acknowledgements

The work is supported by projects of: NATO CLG: ESP.EAP.CLG.983876, ESA C1P-6051 and BG051PO001-3.3.04/40.

4. Conclusion

In this paper an algorithm for synthesis of an interferogram based on digital elevation map (DEM) of Dilijan region in Armenia is made. Using such kind of data a SAR interferometric pair is produced by measurements of the distances from the two SAR satellites to each pixel of the area at the time instant of the SAR image reconstruction. From the interferometric pair the unwrapped phases are calculated and then the wrapped phases are discovered. As a consequence, the interferometric fringes of the surface of interest are generated. A numerical experiment in MATLAB environment is carried out and the results are provided. The algorithms suggested in this study can be exploited for the purposes of simulating, modeling and investigating InSAR processing steps, and generating the synthetic interferograms of a particular area on the Earth. The algorithm for synthesis of SAR interferogram presented in this paper makes it possible to explore real phenomena observed on the ground surface of interest without having real satellite data.

References

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[11] Pi, Y., H. Long, Sh. Huang. A SAR parallel processing algorithm and its implementation, Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002 Conference Proceedings.

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Author’s biography

Prof. Andon Lazarov He received the M.S. degree (1972) from Sent Petersburg Electro technical State University, Russia, in electronical engineering, PhD. degree (1978) from Minsk Air-Defense Military Academy, Belarus and D.Sc. degree (1998) from Shoumen Artillery and Air Defence University. From 1984 he is Associate

Professor and from 2000 to 2002 he is a Professor at the Air Defence Department of the Artillery and Air-Defence University. From 2002 he is a Professor in Burgas Free University. He teaches Discrete Mathematics, Coding theory, Antennas and Propagation, Communication circuits, DSP, Mobil Communications. His field of interest includes SAR-ISAR-InSAR modelling and signal processing techniques (parametric and non-parametric image reconstruction and autofocusing methods, recurrent and iterative LMSE procedures). He has authored above 100 research journal and conference papers. He is a member of the IEEE, AES Society of USA, a member of Trans Black Sea Region Union of Applied Electromagnetism of Greece a guest-editor of special issue on ISAR signal processing of IET Journal, Canada, and a member in editorial board of Applied Electromagnetism Journal - Greece.

Assist.Prof. Dimitar Minchev He received his Bachelor degree of Informatics (2003) from Burgas Free University, Bulgaria, and his Master degree of Informatics (2007) from Shumen University "Episkop Konstantin Preslavski", Bulgaria. Currently he is a PhD student at the Bulgarian Academy of Science at the Institute of Information and Communication Technologies. Since January 2007 he has been working as an Assistant Professor in the Faculty of Computer Science and Engineering in Burgas Free University, Bulgaria. His scopes of scientific interests are: Computer Science, Computer Architectures and Networks, Digital Signal Processing.