In this paper the Modified Fractal Signature (MFS) method is applied to real Synthetic Aperture Radar (SAR) images provided to our research group by SET 163 Working Group on SAR radar techniques. 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 Area curve and Fractal Dimension curve of the image. The main idea concerning this proposed technique is the fact that different terrain types encountered in SAR images yield different values of Fractal Area curves and Fractal Dimension curves, upon which classification of different types of terrain is possible. As a result, a classification technique for five different terrain types (urban, suburban, rural, mountain and sea) is presented in this paper.

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In this paper an application of an autofocusing algorithm, previously developed by the authors for the case of Inverse Synthetic Aperture Radar (ISAR) [i.e. airborne radar targets], is presented here for the case of Synthetic Aperture Radar (SAR) geometry [i.e. ground radar targets]. Here, a new theory and methodology for generating SAR synthetic backscattered data is also developed. This algorithm is named 'CPI-split-algorithm', where CPI stands for 'Coherent Processing Interval'. Moreover, two simulation scenarios are presented for a ship target, which is located on the sea surface. In the first simulation scenario, the ship is considered at first to be stationary, and subsequently an oscillatory movement is induced to its position along the vertical axis, due to sea surface motion. In the second simulation scenario, a partial loss of data is examined, caused by temporary accidental malfunctioning of radar transmitter or receiver, assuming that the target (ship) is stationary. Numerical results presented in this paper show the effectiveness of the proposed autofocusing algorithm for SAR image enhancement.

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Rbsrracr-Time -frequency analysis i s nowadays very rrequently used for the evaluation of non -stationary signals. with applications in areas such as radar target imaging and identification, seismic signal interpretation etc. The corresponding two -dimensional (ZD) time -frequency plots, usually called 'spectrograms', are sometimes very useful, because they provide the time -dependence of the signal spectrum, not available in other traditionat spectrum estimation methods.In this paper we focus on several time -frequency techniques, like the Short -Time Fourier Transform (STFT). Furthermore, the performance of the 3ilinear Time -Frequency Transforms is also carefully e.x.amined.The basic idea of time -frequency analysis is the characterization of the time-varying frequency content of a signal. I n this way, additional signal information can be acquired, and ultimately improved target image resolution can be achieved. Finally, time -frequency transforms allow the use of variable parameters, which chsnge according to the time and frequency, in order to achieve the desired target resolution.In this paper, we develop computer codes for the above methods, and simulated synthetic radar data are used for their implementation.