| Summary: | Applications of self-illuminating remote sensing systems, and among those, Radar Imagery is growingrapidly. Unique properties of Synthetic Aperture Radar (SAR) system makes it one of the most popularand applicable methods of self illuminating remote sensing techniques for ground deformationmonitoring, seismic studies, and many photogrammetry applications.There are several methods and algorithms for processing SAR data, each are convenient for differentpurposes. Two more common and reliable algorithms are developed in this thesis: a Range DopplerAlgorithm and a Chirp Scaling Algorithm.Available software packages and toolboxes for processing SAR data such as DORIS, ROI-PAC, RAT andPULSAR have their advantage and disadvantages. Most of these packages run on Linux platform, aredifficult to use, and require quite a few pre-processing data preparations. Besides there is no general SARprocessing application that can handle all data types or suitable for all purposes. There are also softwarepackages (such as ROI-PAC) with restrictions for people from certain countries.The objective of this thesis is to process SAR data using two more common algorithms, run a comparisonbetween results of these two algorithms and to process InSAR pair images to form an Interferogram andto create a DEM. A Matlab based program is developed for this purpose with graphical user interface anda few visualization enhancement features, which facilitates processing data and producing desired output.Then, I investigate the effect of different frequency domains in the resulting image.The program I created in my thesis, has several advantages: it is open-source and very easy to modify.The program is coded in MATLAB, therefore it does not need a vast programming knowledge to be ableto customize it. You can run it on any platform that can run MATLAB 7+.At the end of this thesis, I conclude that A Range Doppler Algorithm with secondary range compressionperformed in 2D-frequency domain has a result as good as a Chirp Scaling Algorithm and has lesscomputational complexity and consumes less time. No general SAR processing algorithm could beintroduced. Most of the times algorithms need to be adjusted for particular datasets, or particularapplications. Besides, the most complicated algorithm is not always the best algorithm. For example, for apoint target detection purpose, two filtering steps in range and azimuth direction provides accurateenough result.
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