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Title: An Algorithm for SAR Image Embedded Compression based on Wavelet Transform

Description:



Synthetic Aperture Radar (SAR) image compression is important in image transmission and archiving. In this paper, SAR image compression using embedded zerotree wavelets algorithm, based on discrete wavelet transform (DWT), is researched. Aiming at special characteristics of SAR image such as speckle noise, the author added a denoise step in the flow of compression, trying to do some meaningful attempt in SAR image compression. Experiments carried out show that the improvement can reduce the speckle noise and improve the computation precision and time.

Comparison of simulation results Matlab was done to verify the proper functionality of the developed module. The motivation in designing the hardware modules of the FDWT was to reduce its complexity, enhance its performance and to make it suitable development on a reconfigurable FPGA based platform for VLSI implementation. Results of the decomposition for test image validate the design. The entire system runs at 215 MHz clock frequency and reaches a speed performance suitable for several realtime applications. The result of simulation displays that lifting scheme needs less memory requirement.

INTRODUCTION:



Synthetic aperture radar (SAR) is a very efficient instrument for obtaining a better understanding of the environment. SAR image products are very important and useful for remote sensing applications because they can be acquired independent of time or day or weather conditions and because their characteristics (wavelength, polarisation, observation angle) can be chosen in function of the phenomenon under investigation. So SAR image compression is important in image transmission and archiving. Due to the high entropy of SAR raw data, conventional compression techniques fail to ensure acceptable performances, in that lossless ones do not in fact succeed to compress, while general purpose lossy ones (e.g. JPEG) provide some compression degree only at the price of unacceptable image quality degradation[1]. Furthermore the presence of speckle noise in SAR images limits the visual interpretation of scenes because it obscures the content. In order to get reliable data interpretation and quantitative spots measurements, it is recommended to applying speckle filtering schemes in SAR image compression

DESCRIPTION:



The enhancement & utilization of image process in typical environment makes the project development more realist.

In this project, it deals that a image i.e., a sar image, which make includes noise during the tracing of image from remote areas for any particular application. As and when the image receiving the noise added makes chance of distortion in the original image.

As to have exact picture details of particular target or area of concern…. It is necessary to eliminate the noise as possible as much. By making use of different denoising techniques. After removing the noise, as the size of picture details takes more memory size, to reduce the no of pixel rate such that the memory size is reduced makes obviously compression. As the amount of compression we make the rate of picture quality decreases. So , it is trade off between the compression and the picture quality, so choose optimum compression such that the picture should fine to exact the target information.

SAR image compression using embedded zerotree wavelets algorithm, based on discrete wavelet transform (DWT), is researched. Aiming at special characteristics of SAR imagery such as speckle noise, the author added a denoise step in the flow of compression, trying to do some meaningful attempt in SAR image compression. Experiments carried out show that the improvement can reduce the speckle noise and improve the computation precision and time.

Synthetic-aperture radar (SAR) is a form of radar in which multiple radar images are processed to yield higher-resolution images than would be possible by conventional means. Either a single antenna mounted on a moving platform (such as an airplane or spacecraft) is used to illuminate a target scene or many low-directivity small stationary antennae are scattered over an area near the target area. The many echo waveforms received at the different antenna positions are post-processed to resolve the target. SAR can only be implemented by moving one or more antennae over relatively immobile targets, by placing multiple stationary antennae over a relatively large area, or combinations thereof. SAR has seen wide applications in remote sensing and mapping.

In a typical SAR application, a single radar antenna is attached to the side of an aircraft. A single pulse from the antenna will be rather broad (several degrees) because diffraction requires a large antenna to produce a narrow beam.

The pulse will also be broad in the vertical direction; often it will illuminate the terrain from directly beneath the aircraft out to the horizon. If the terrain is approximately flat, the time at which echoes return allows points at different distances to be distinguished. Distinguishing points along the track of the aircraft is difficult with a small antenna. However, if the amplitude and phase of the signal returning from a given piece of ground are recorded, and if the aircraft emits a series of pulses as it travels, then the results from these pulses can be combined.

Effectively, the series of observations can be combined just as if they had all been made simultaneously from a very large antenna; this process creates a synthetic aperture much larger than the length of the antenna (and much longer than the aircraft itself).

A scale hologram interference pattern was produced Combining the series of observations requires significant computational resources. It is often done at a ground station after the observation is complete, using Fourier transform techniques. The high computing speed now available allows SAR processing to be done in real time onboard SAR aircraft. The result is a map of radar reflectivity (including both amplitude and phase). The phase information is, in the simplest applications, discarded. The amplitude information contains information about ground cover, in much the same way that a black-and-white picture does. Interpretation is not simple, but a large body of experimental results has been accumulated by flying test flights over known terrain.

Image resolution of SAR is mainly proportional to the radio signal bandwidth used and, to a lesser extent, on the system precision and the particular techniques used in post-processing. Early satellites provided a resolution in the tens of meters. More recent airborne systems provide resolutions to about 10 cm, ultra-wideband systems (developed and produced in the last decade) provide resolutions of a few millimeters, and experimental terahertz SAR has provided sub-millimeter resolution in the laboratory.

Before rapid computers were available, the processing stage was done using holographic techniques. This was one of the first effective directly from the analogue radar data (for example 1:1,000,000 for 0.6 meters radar). Then laser light with the same scale (in the example 0.6 micrometers) passing through the hologram would produce a terrain projection. This works because SAR is fundamentally very similar to holography with microwaves instead of light.

The microwave beam sent out by the antenna illuminates an area on the ground (known as the antenna's "footprint"). In radar imaging, the recorded signal strength depends on the microwave energy backscattered from the ground targets inside this footprint. Increasing the length of the antenna will decrease the width of the footprint.

Interaction between Microwaves and Earth's Surface

When microwaves strike a surface, the proportion of energy scattered back to the sensor depends on many factors:

• Physical factors such as the dielectric constant of the surface materials which also depends strongly on the moisture content;

• Geometric factors such as surface roughness, slopes, orientation of the objects relative to the radar beam direction;

• The types of landcover (soil, vegetation or man-made objects).

Microwave frequency, polarisation and incident angle.

WHY DO WE NEED TRANSFORMS?



A Transform is a mathematical operation that takes a function or sequence and maps it into another one. Transforms are used because

a) The transform of a function may give additional /hidden information about the original function, which may not be available /obvious otherwise.

b) The transform of an equation may be easier to solve than the original equation (recall Laplace transforms for “Diff- Equations”).

c) The transform of a function/sequence may require less storage, hence provide data compression/reduction.

d) An operation may be easier to apply on the transformed function, rather than the original function (recall convolution).

Mathematical transformations are applied to signals to obtain further information from that signal that is not readily available in the raw signal. Most of the signals in practice, are Time domain signals in their raw format, i.e. whatever that signal is measuring, is a function of time.

In other words, when we plot the signal, one of the axes is time (independent variable), and the other (dependent variable) is usually the amplitude. When we plot time-domain signal we obtain a Time – Amplitude representation of the signal. This representation is not always the best representation of the signal for most IMAGE PROCESSING related applications.

In many cases, the most distinguished information is hidden in the frequency content of the signal. The frequency spectrum of a signal is basically the frequency components (spectral components) of that signal. The frequency spectrum of a signal shows what frequencies exist in the signal.

Intuitively, we all know that the frequency is something to do with the change in rate of something. If something (a mathematical or physical variable would be the technically correct term) changes rapidly, we say that it is of high frequency, where as if this variable does not change rapidly, i.e., it changes smoothly, we say that it is of low frequency.

If this variable does not change at all, then we say it has zero frequency, For example the publication frequency of a daily newspaper is higher than that of a monthly magazine. Frequency is measured in cycles/second, or with a more common name, in "Hertz". Now, look at the following figures. The first one is a sine wave at 3 Hz, the second one at 10 Hz as shown below.

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