82

Chapter 4

SAR Data and Pre-Processing

4.1 Introduction

A typical SAR can be broken up into its major subsystems, as shown in figure 4.1.

The radar functions by first having the radio frequency (RF) electronics send pulse-

compressed radar pulses and then receiving the backscattered echoes using the same

SAR antenna. The RF electronics then down convert to baseband and amplify the

received signal before splitting the demodulated signal into its real (or in-phase, I)

and imaginary (or quadrature, Q) components for further processing by the digital

electronics.

SAR Antenna Down link Antenna

I

Q

Figure 4.1: Block diagram of SAR sub-systems

All conventional SARs digitize the I and Q signals using high-speed Analog to

Digital Converter (ADC), passing the digitized SAR signal to the digital electronics

subsystem.

RF Electronics

ADC

ADC

Digital Electronics Sub-system

Downlink Transmitter

Storage

83

The digitized SAR signal, called the raw SAR data, is then framed for on-board

storage or transmission to the ground station. It is at this stage that compression of the

raw SAR data can occur. In the event that the satellite is not in a position where it can

communicate with the ground station, on-board storage is used for later transmission.

SAR data acquired from satellite are in time domain and they are usually transformed

in other domains for computational reasons. These domains are two-dimensional

frequency domain and range Doppler domain. Using these domains enables to

produce proper match filters for processing SAR data. Transmitted and received

pulses in a SAR system are real signals. To transfer received signals to baseband

signal it has to be band shifted by a quadrature demodulation process as shown in

figure 4.2.

cos (2πf0 τ) Real

Channel (XR)

x(τ)

Imaginary

Channel (Xi)

- sin (2πf0 τ)

Figure 4.2: Quadrature Demodulation Process

The demodulation removes the high frequency carrier, but may create some signal

errors. This process transforms the signal into two channels, Real and Imaginary

channels. The real channel is created first by multiplying the signal in cos (2πf0 τ).

The imaginary channel is extracted in a similar way by multiplying the signal by - sin

(2πf0 τ). The two separated signals are called the quadrature components of a complex

signal or I and Q channels.

LPF

LPF

ADC

ADC

84

4.2 SAR Data Extraction

SAR system gives a data file consisting of signal properties of transmitted and

received signals, plus auxiliary data defining parameters regarding the satellite orbit,

speed and other characteristics. All these information is stored in a particular file

structure and saved as the “RAW Data File”. The very first step, yet most important

and very complicated step of SAR processing, is to read the satellite RAW Data File

and extract desired data. Different SAR data sources provide data files in different

formats and with different file structures. To read the required data from RAW Data,

we have to know the file structure and format, and we need information about how

data is stored in the file.

We have used ERS-2 and RADARSAT-2 data and focused our work on these

particular SAR systems. RADARSAT-2 Raw Data is stored in the CEOS format. The

first 192 lines of the file are file header which contains information regarding time

and date of data acquired, plus satellite orbit information. Then, there are 50 lines

where auxiliary data is stored. After these lines, the main data body is located, where

data for transmitted and reflected data is stored. RADARSAT data is stored on 8-line

blocks structure. This means received signal from 8 transmissions in azimuth

direction is stored together with one replica of transmitted signal. The collected data

is encoded and then stored in the Raw Data File.

To extract a set of desired data, relevant lines of data file are read, then the Real and

Imaginary parts of the complex signal is extracted, the data is decoded and then,

extracted data is saved in the shape of a matrix consisting of complex numbers. In this

program where we want to extract a certain window of the whole data, the program

first finds the starting line of data file corresponding to the selected window. The

starting line has to be adjusted, since data is stored on the basis on 8-line blocks.

Therefore the selected window automatically moves to start line of the corresponding

data block. Then program starts extracting data from that point. The reason is that,

source data file is a large file (about 400 MB) which contains data for about 180

85

million cells, means 360 million real and imaginary numbers. It is almost impossible

to load the whole data on a PC and then look for the selected window.

When the starting point of extraction is defined, program starts reading rows of the

selected window completely, at the same time it separates the replica data and radar

echo data. Then the program extracts exact selected window by cutting out

unnecessary extracted columns and then demodulates the data to complex numbers

containing I and Q channels of the reflected signal. The result still has to be pre-

processed to get ready for next steps of SAR processing. First it is decoded and then

Gain-corrected to eliminate the effect of attenuation variation. The result of these

steps will be a file containing a variable called “data” which is a matrix by the size of

selected window containing complex numbers which are reflected signals from the

earth surface. If an image is created by calculating the magnitude of unprocessed

received signal it can be observed as full of noise.

4.2.1 CCSD Raw Data The data product has two files. One containing the data for the image and the other

containing information about the data, the image, and information to go with the

image. The data file, one of the files, contains nothing but image data. It is simply a

huge chunk of continuous data. (It has an extension .D) The other type of file is the

metadata file, which contains all the gathered information about the data. (Extension

".L" with the same base name as ".D" file).

The metadata file contains important information for viewing the image,

manipulating the image, processing the image, and archiving the image. First, the

metadata file contains the image information such as the range (width) and azimuth

(length or height) of the image. Along with the image information, the metadata

contains information such as the position and orientation of the satellite at the time,

the image data was gathered.

86

4.2.2 CEOS Image Data

CEOS Data is one of the main types of data that the Alaska SAR Facility (ASF)

produces. There are three main levels of CEOS data. The first, known as CEOS Level

0 data, has two main formats. The first format is the raw stream of data transmitted

from the satellite. This data is simply the unformatted jumble of bits received from

the satellite. The second format of CEOS Level 0 data is the formatted stream of

satellite data. This has the sync codes removed and is byte aligned for ease of

integration into a computer system. CEOS Level 0 data must be processed further to

yield useable data products such as images and other derived data. This further

processed data is known as CEOS Level 1 data.

CEOS Level 1 data is mainly in the form of images that are derived from CEOS

Level 0 data. The level of processing done on the data varies from the raw image,

such as what the satellite "sees,” to flatten out geocoded images. This processing is

done by the different tools developed and maintained at ASF. Further processing of

these images, results in data types known as CEOS Level 2 data. Such things are

usually the result of multiple images and their comparison. Data such as ice motion

and Interferometry are examples of CEOS Level 2 data. In the present thesis we are

using level 1 data in the form of SAR images.

4.2.3 File Decoding

Consider the scene indicator: E222361290S0C014 (data file name also is the same

character appended by .D) where:

E2 ---- for ERS-2

22361 ---- Orbit Number (ranges from 00000 to 99999)

290 ---- Fixed frame numbering scheme relative to ascending node

S ---- Indicates Slant range (Projection type of Data)

0 ---- for Data Pixel Spacing = 6.25

C ---- For CCSD data type

14 ---- Version Number

87

SAR processing is so computationally intensive, that it is the slowest part of

interferometry processing. Sun Microsystems SPARC Server 1000, processing one

full frame 5,120 samples by 24,000 line image using AISP processor takes a little

over an hour. On an SGI Origin 2000, processing one full frame complex product

takes about 15 minutes. To speed this up, parallel implementation of SAR processor

(PAISP) is required. Running on 56 processor elements of the Arctic Region

Supercomputing Center’s Cray T-3E massively parallel processor, can process the

same image in less than 90 seconds.

Since the original 8-bit sampled data on the satellites are not available to the general

public, the available images are to be conditioned to the 8 –bit for experimental use.

We are using three scenes to process through the SAR processor. We are not using

ASF-SAR processor meant for CCSD data but instead we have developed the SAR

processor to process the input CEOS level-1 data available in the form of SAR

images procured from ERS-2, RADARSAT-2 and TerraSAR-X satellite and released

for experimental use by the respective agencies. The original SAR images are big in

dimension so, we crop the images to 256 x 256 pixel size for the present work. The

technical parameters of the image and SAR instrument are summarized in the

appendices attached at the end of the thesis.

4.3 Selection of Scenes

The criteria for selection of scenes were that the images should have good contrast,

have good feature diversity, and be well scaled. The existence of pre-scaled point

targets is also desirable to analyze the point target performance of the compression

algorithms. Good feature diversity facilitates the evaluation of the algorithms in

feature identification and visual image quality measurements. Also scenes from

different passes of the satellite are deemed to be useful in evaluating the performance

of the algorithm under different imaging conditions.

The scenes selected for this research work have been taken from an urban city area

close to European Space Research and Technology Centre (ESTEC) in Netherlands

88

acquired by C-band ERS-2 SAR. The scenes have a variety of features including

fields, roads, canals, etc.

Another image from ERS-2 is a stretch of the Inn River along the German / Austrian

border in the vicinity of Braunau. The image has been selected to monitor the river as

it is experiencing surges following Alpine Valley flood. One more image is from

high resolution X band (16.8 GHz) miniSAR image of U.S. capitol and library of

Congress, Washington D.C. supplied by Sandia Laboratory of NASA, USA.

4.4 Speckle formation

When radar illuminates a surface that is rough on the scale of a radar wavelength, the

return signal consists of waves reflected from many elementary scatterers within a

resolution cell. The distance between the elementary scatterers and the receiver vary

due to the surface roughness. Therefore, the received waves, although coherent in

frequency, are no longer coherent in phase. If the waves add relatively constructively,

a strong signal is received: otherwise a weak signal may be received due to

destructive combination of out of phase waves. A SAR image is formed by coherently

processing the returns from successive radar pulses. The result is pixel to pixel

variation in intensity and this variation manifests itself as a granular pattern, called

speckle. We see that a zone that is homogenous on the ground can have a granular

aspect is a SAR image with large variation in pixel intensities; moreover in

heterogeneous regions of such an image, speckle noise can obscure image details by

randomly modifying the pixel intensities. These effects may prohibit the abilities of

human or computer vision systems to extract information from SAR images. Thus

although speckle noise carries information about the microstructure of an image area,

speckle is usually treated as noise in image processing applications.

4.5 Speckle Noise Filters (Pre-filtering)

Since speckle noise contaminates image content and thus detracts from image

interpretation, speckle noise reduction is usually employed prior to further image

89

analysis. The primary goal of speckle filtering is to reduce speckle noise without

sacrificing information content. The ideal speckle filter should adaptively smooth

speckle noise, retain edges and features, and also preserve subtle but distinguishable

details, such as thin linear features and point targets. Various speckle filters (Lee et

al, 1994, Chitroub et al, 2002, Touzi 2002, Walesa and Datcu, 2000) have been

devised due to their different purposes and different capacities. We list out some of

the popular techniques like Adaptive mean filter, Lee multiplicative filter, Frost filter,

Sigma filter, Median filter, Kalman filter and Wienier2 filter etc. Out of these filters,

to carry out the present study ,we are analyzing median, Lee and Wiener2 filters for

the sampled SAR images and comparing their performances statistically by analyzing

data mean, standard deviation, kurtosis and skewness as well as by comparing the

visual quality of the input images and de-speckled images.

4.5.1 The mean filter

The mean filter is a simple averaging filter that replaces a centre pixel of a sliding

window by the mean value of the pixels in its neighbor window. This filter has a good

noise smoothing capability but loose the resolution i.e. blurring in the vicinity of

sharp edges.

4.5.2 The median filter

With a median filter and its variations the centre pixel of a sliding window is replaced

by the median intensity of all pixels within this window. Although this filter is

capable to remove impulse or short duration noise, it is not well suited for speckle

noise. Edge blurring, erasing thin linear features and object shape distortion are the

common problems of this filter.

4.5.3 Wiener2 filter

Wiener2 Perform 2-D adaptive noise-removal filtering. WIENER2 low pass filters an

intensity image that has been degraded by constant power additive noise. WIENER2

uses a pixel-wise adaptive Wiener method based on statistics estimated from a local

90

neighborhood of each pixel. The input image can be of class uint8, uint16, or double.

The output image is of the same class as that of input image.

4.5.4 The Lee multiplicative filter

The Lee multiplicative filter is designed to overcome difficulties of indiscriminate

averaging of the mean filter and is based on the multiplicative speckle model, as

adopted and verified by Lee. If x y and n are the input signal, output signal and the noise, then they can be related as , , , 4.5.1

Lee assumed that the mean and variance of the noise free original image x can be

estimated from the local mean and variance of the observed image y .Thus

, , ,

, , , 4.5.2

Where and var(y) (i,j) are approximated by the sliding window mean and variance

respectively with the assumption that 1 . can be minimized to yield

the estimated noise free image , i.e.

, , , , , 4.5.3

Where

, ,, ,

91

This implies that in homogeneous areas, the local variance is close to 0, thus the

filtered pixel is set to the average of pixels in the window. For high contrast regions

or the edge areas where the local variance is usually larger, the pixel value is

unchanged in order to preserve the feature. Although the assumption that , 1

is made in this algorithm, this restriction is not severe since any bother value of can

be factored into the equation above. The value of ,which is a measure of speckle

strength, can be calculated be the following relation.

4.5.4

The lee multiplicative filter (Lee, et al, 1994) is the most famous speckle filter,

effectively reducing the speckle in the homogenous areas so we will be using this

filter for pre-processing of the SAR image

4.4.5 The sigma filter

The sigma filter is based on the sigma probability of a Gaussian distribution. It filters

the image noise by averaging only those pixels within the two standard deviation (2σ)

range of the center pixel within a sliding window. Pixels outside two sigma are

considered as outliers and ignored. Thus the estimated pixel intensity , can be

expressed as

, 1 , . , 4.5.5

Where y(i,j) is the noise degraded image intensity. Ne is the total number of pixels

summed and δ (k,l) is defined as

,1 , 2 ,0 4.5.6

92

Consequently, high contrast features are preserved. However, the dark spot noise is

not removed from the SAR image. This is due to relatively small variation range

associated with dark pixels corrupted by the speckle noise and as a result no filtering

action is taken for such pixels.

Some other filters like multiplicative model based spatial domain adaptive Wiener

filter i.e. Frost filter, geometrical filter, the morphological filter, the 2D block kalman

filter ,the rational filter (Walessa and Daleu, 2000) and soft-thresholding denoising

method(Zhou and Zhao, 2009) are also tested for SAR images for speckle reduction

and give good results.

4.6 Data statistics and Pre-Conditioning

The data compression technique used on-board ERS-2; TerraSAR-X and

RADARSAT-2 satellite is dynamic range reduction. This means that of the 8 bits

available on the satellite, if an 8 bit ADC had been used, only 5- bits or 4-bits for the

satellite were transmitted. In order to make these 4 or 5 bits data into 8 bits real SAR

data, 3 or 4 bits of the additional simulated data are required to be added, when data

being used are of CCSD level 0 forms. But CEOS level 1 images , being used

presently contains lot of speckle noise with them during acquisition and processing

so image de-speckling is used to remove the multiplicative speckle while retaining as

much as possible the important signal features. Figure 4.4 to 4.7 show histograms

and normal probability plots of a block of 256 x 256 pixels of the images (CEOS-

level 1 data) obtained from ERS-2, Terra-SAR-X and RADARSAT-2 satellites.

93

Figure 4.4: Histogram and Normal probability plot of Input Image and De-speckled Image

(RADARSAT-2)

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

10000

Pixel value

Num

ber o

f Pix

els

Histogram of Input Image(RADARSAT-2)

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of Input Image

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

10000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(median filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99 0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of De-noised Image

-150 -100 -50 0 50 1000

2000

4000

6000

8000

10000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Lee filter) Image

-100 -50 0 50

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

-150 -100 -50 0 50 100 1500

2000

4000

6000

8000

10000

12000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Wiener2 filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

94

Figure 4.5: Histogram and Normal probability plot of Input and De-noised Image (TerraSAR-X)

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

10000

12000

Pixel value

Num

ber o

f Pix

els

Histogram of Input Image(TerraSAR-X)

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of Input Image

-200 -150 -100 -50 0 50 100 1500

5000

10000

15000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(median filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value dataPr

obab

ilit

y

Normal Probability P lot of De-noised Image

-200 -150 -100 -50 0 50 100 1500

5000

10000

15000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Lee filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

-200 -150 -100 -50 0 50 100 1500

5000

10000

15000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Wiener2 filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

95

Figure 4.6: Histogram and Normal probability plot of Input and De-noised Image (ERS-2)

-200 -100 0 100 2000

1000

2000

3000

4000

5000

6000

Pixel value

Num

ber o

f Pix

els

Histogram of Input Image(ERS-2)

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of Input Image

-200 -100 0 100 2000

1000

2000

3000

4000

5000

6000

7000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(median filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value dataPr

obab

ilit

y

Normal Probability P lot of De-noised Image

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Lee filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

-150 -100 -50 0 50 100 1500

2000

4000

6000

8000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Wiener2 filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

96

Figure 4.7: Histogram and Normal probability plot of Input and De-noised Image (US Library Congress)

The shape of the resulting histograms and normal probability plots (fig. 4.4 to fig.

4.7) of the CEOS level-1 image data after application of median filter, Lee filter and

Wiener2 filters is smooth and the saturation at the tails is less than the original data.

-300 -200 -100 0 100 2000

2000

4000

6000

8000

10000

12000

14000

Pixel value

Num

ber o

f Pix

els

Histogram of Input Image(US Library Congress)

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of Input Image

-200 -150 -100 -50 0 50 100 1500

5000

10000

15000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(median filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot of De-noised Image

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Lee filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

-200 -150 -100 -50 0 50 100 1500

2000

4000

6000

8000

10000

Pixel value

Num

ber o

f Pix

els

Histogram of denoised(Wiener2 filter) Image

-100 -50 0 50 100

0.0010.0030.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.98 0.99

0.9970.999

Sample value data

Prob

abil

ity

Normal Probability P lot

97

There are, however, small local regions of higher than expected probability close to

the tails as a result of the tail saturation of the original data. Upon examination of the

other statistical requirements of a data pre-conditioning technique, it can be seen that

the multiplicative model based spatial domain Lee filter produces good results. The

statistics of one of the typical image from TerraSAR-X image and the resulting de-

noised or pre-conditioned image are shown in table 4.6.1

Table 4.6.1: Statistics of the original and median filter De-noised image

Statistics Original Image

(TerraSAR Image)

Pre-Conditioned (De-noised) Image

Median filter Lee filter Wiener2 filter

Mean(µ) 88.9378 86.7652 88.1529 89.2420 Standard

Deviation(σ) 56.4924 48.0599 46.4016 44.0272

Kurtosis 3.7566 3.9167 3.1441 3.5896 Skewness 0.7968 0.6140 0.4078 0.6443

ENL(µ2/σ2) 2.4785 3.2593 3.6091 4.1086 Entropy 3.4113 3.2620 7.4175 7.4054

Redundancy 4.5887 4.7380 0.5825 0.5946

Kurtosis (k) describes how peaky or flat the distribution is, with reference to the

Normal distribution. Positive kurtosis indicates a more peaked distribution and

negative kurtosis indicates a flatter distribution thus kurtosis is a measure of how

outlier-prone a distribution is. The kurtosis of the normal distribution is 3.

Distributions that are more outlier-prone than the normal distribution have kurtosis

greater than 3; distributions that are less outlier-prone have kurtosis less than 3. K is

the fourth central moment of X, divided by fourth power of its standard deviation.

The kurtosis of a distribution is defined as

4.6.1

Where µ is the mean of x, σ is the standard deviation of x, and E (t) represents the

expected value of the quantity t.

98

Skewness (s) is a measure of the asymmetry of the data distribution around the

sample mean. If skewness is negative, the data are spread out more to the left of the

mean than to the right. If skewness is positive, the data are spread out more to the

right meaning long right handed tail. The skewness of the normal distribution (or any

perfectly symmetric distribution) is zero. S is the third central moment of x, divided

by the cube of its standard deviation. The skewness of a distribution is defined as 4.6.2

Where µ is the mean of x, σ is the standard deviation of x, and E (t) represents the

expected value of the quantity t.

Effective Number of Looks (ENL) is another important parameter to measure the

performance of the filtered image. Higher the value of the ENL better it is. This is

defined as

4.6.3

Where, σ, is the standard deviation and µ is the mean of the image. This equation

equates how smooth the image is. The ENL provides a good measure of the

performance for flat terrain but generally not for terrain with high relief. To obtain

improved ENL the image should be broken down into smaller sectors and the ENL of

these sectors are averaged. Though the drawback of the ENL measurement is that it

cannot detect terrain loss through the filtering of the image.

Entropy is a measure of information in an image and redundancy indicate the

surplus number of bits being used while encoding the image. Greater the entropy (and

hence lesser the redundancy) better it is. It is observed that filtering increases entropy

of the image.

99

Figure 4.8: Input and De-noised (Median Filter, Lee filter and Wiener2 filter) Images from

RADARSAT -2 and ENVISAT

Input Image (RADARSAT-2)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Input Image (ENVISAT)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

100

Figure 4.9: Input and De-noise (Median Filter, Lee filter and Wiener2 filter) SAR Images of

US library congress and Hector mine.

Input Image (US Library Congress)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Input Image (Hector Mine)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

101

Figure 4 .10: Input and De-noise (Median Filter, Lee filter and Wiener2 filter) Images

TerraSAR-X

TerraSAR-X Input Image (Copper Mine Hole)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

TerraSAR-X Input Image (Viedma Glacier)

50 100 150 200 250

50

100

150

200

250

Median filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Lee-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

Wiener2-filter de-noised Image

50 100 150 200 250

50

100

150

200

250

102

4.7 SAR Processing Algorithm

Once the SAR data have been received at the ground station it must be processed to

form the final SAR product. It is this processing step that correlates all Doppler

components for ground targets. The conventional and most widely seen SAR product

is the SAR amplitude image. There are several SAR processing algorithms available,

and each has its advantages and disadvantages. Some of these algorithms are:

- Range Doppler Algorithm

- Chirp Scaling Algorithm

- Omega-K Algorithm

- SPECAN Algorithm

The first algorithms is implemented and discussed in this thesis.

4.7.1 Range Doppler Algorithm (RDA)

The range Doppler algorithm (RDA) was developed in 1976-1978 for processing

SEASAT SAR data. Later it was used to digitally process spaceborne SAR image in

1978 and it is still the most widely used algorithm today. RDA operates in range and

azimuth frequency domain, but it has the simplicity of one dimensional operations.

The reflected energy from areas on the earth’s surface in the same range but in

different azimuth, are located on the same azimuth frequency. So, when this

frequency is adjusted, the whole target areas with the same frequency (which means

in the same range) are adjusted. RDA uses the large difference in time scale of range

and azimuth data and approximately separates processing in these two directions

using Range Cell Migration Correction (RCMC). RCMC is the most important part of

this algorithm. RCMC is performed in range frequency and azimuth frequency

domain. Since, azimuth frequency is affected by Doppler Effect and azimuth

frequency is bonded with Doppler frequency, it is called Range Doppler Algorithm.

The main steps of RDA implementation are:

103

1- Range compression

2- Azimuth FFT (transform to range Doppler domain)

3- Range Cell Migration Correction (RCMC)

4- Azimuth filtering

5- Inverse FFT (return to range azimuth time domain)

6- Azimuth Compression and Image formation

But when it comes to implementing the code for this algorithm this is what exactly it

takes practically:

1- Defining auxiliary data and parameters

2- Converting data to 2D frequency domain

3- Creating Match filter and performing Range Compression

4- Performing Range Cell Migration Correction

5- Converting Range Compressed data to Range-Doppler domain

6- Creating Match filter and performing Azimuth Compression

7- Returning data to Time-Domain

8- Visualization of results

Depending on the filter type and reference function we create, Range Compression

and RCMC have to be applied in relevant domains. Processing steps are described in

this section

Step 1

Parameters regarding satellite and selected window are loaded. Some new parameters

are defined;

τ: fast time, this is the time difference between range samples. It is defined as a

vector starting from the observation time of first sample in range and ending by the

time of the last range sample, with 1/Fr as steps, where Fr is the range sampling rate.

fa and fr: are the frequency sampling rate in azimuth and range direction. These

sampling rates are not depending on how many samples we use. This means, if we

have only 4 samples in the time domain and we do the Fast Fourier Transform, we get

104

the complete bandwidth of frequencies defined by the sampling rate. But it creates a

very coarse frequency resolution with frequency steps equal to PRF/echoes; Where

PRF is the Pulse Repetition Frequency. A better frequency resolution is achieved with

larger windows.

Step 2

Converting data to other domain is a very useful method to facilitate data processing.

Range- Doppler domain is processing domain for many of SAR processing

algorithms, because it simplifies the processing to one-dimensional environment.

Data is converted to range and azimuth-frequency domain (range Doppler) by

performing Fourier Transformation on every column of data (azimuth fft). In

MATLAB this is simply done by the command fft which stands for “Fast Fourier

Transformation”. The next step is to transform data to 2D frequency domain. This is

done by performing another Fourier transformation on the rows of data (range fft).

This step is a bit different from azimuth fft, since the phase of receiving signal

changes in range direction. Therefore data has to be zero-padded before

transformation and it has to return to its original arrangement after FFT is performed.

Zero-padding is done in MATLAB by using “fftshift“command. Besides, to perform

fft on rows, matrix of data has to be transposed to enable fft command to perform in

columns.

Step 3

Range compressing can be performed in different domains. In our program we have

chosen to perform range compressing in 2D frequency domain and that is why data is

transformed to 2D frequency domain. To compress data in range, we need to create a

matched filter in frequency domain and finally multiplying every row of data by this

filter. Match filter parameters depend on sampling rate and pulse repetition

frequency. By a quick look at the MATLAB code one can notice it is done by easily

(but yet complicated!) using “ones “function in MATLAB.

Step 4

Range Cell Migration Correction (RCMC) is performed by applying a shift to the

range compressed data. To use this shift function in 2D frequency domain, where we

105

are doing most of computations, the frequency factor in range (f τ) will be included in

the matched filter. This function is then multiplied to range compressed data.

Step 5

In MATLAB inverse Fourier transformation (IFFT) is done by simply using the

command ifft. The data are converted to 2D time domain. At this stage we roll back

one step of Fourier transformation to return range compressed data to Range-Doppler

domain. This is the domain that azimuth compression is performed.

Step 6

Azimuth matched filter is created similar to range matched filter. It is also applied in

the same way as described for range direction.

Step 7

Now all computations are done and data is almost ready to be displayed. The last step

is to return data to Range-Azimuth time domain. This is done by performing an ifft

command in MATLAB.

Step 8

When dealing with RADAR data, it is to be borne in mind that Radar images are

composed of many dots, or picture elements. Each pixel (picture element) in the radar

image represents the radar back scatter for that area on the ground: darker area in the

image represents low backscatter and the brighter areas represent high backscatter.

Bright features means that a large fraction of the radar energy was reflected back to

the radar, while dark features imply that very little energy was reflected. The back

scatter is often related to the size of an object, with objects approximately the size of

the wavelength (or larger) appearing bright (i.e. rough) and objects smaller than the

wavelength appearing dark (i.e. smooth). A useful rule-of-thumb in analyzing radar

images is that higher or brighter the backscatter on the image, the rougher the surface

being imaged. Flat surfaces that reflect little of no microwave energy back towards

that radar will always appear dark in radar images. Radar images consist of very few

bright points and most of the received signal is too weak to show a good contrast on

the image when plotting the magnitude of signals. To solve this issue and produce

better images, data can be displayed in logarithmic mode. In this mode, the magnitude

106

of reflection is computed, then logarithm of this value is computed and image is

created using this value. However, sometimes it is also helpful to see the image in

normal mode where reflectors could be detected easily as it makes no change on the

data. Finally the resultant image can be saved as a .jpg or .tiff file, data as magnitude

and Phase and signals can be saved as complex numbers. The overall algorithmic

flow chart for SAR signal processing (SAR Image formation from raw data) is shown

in figure 4.11.

SAR Raw Data

Data

SAR Image Data

Figure 4.11: SAR signal processing

Range FFT

Range Ref. Multiplication

Range Inverse FFT

Corner turn

Azimuth Ref. Multiplication

Azimuth Inverse FFT

Pre-Processing

Azimuth FFT

Range Reference Generation

Azimuth Reference Generation

107

4.8 Raw Data from Simulated SAR Images (Image

Expansion)

After preprocessing (de-speckling) the sample radar simulated images, expansion is

carried out requiring generation of reference function in range and azimuth directions

separately in MATLAB environment. The reference functions are generated as

follow.

4.8.1 Range Reference Generation

In order to expand the image in range direction we generate a reference chirp signal

known as range reference chirp signal and represented as 1 4.8.1

also .

and where i = No. of samples and fs = Sampling frequency

Typical parameters for Radar Imaging Satellite (RISAT-2) in the present case are

Sampling rate (fs) = 83.3 MHz

Chirp Band Width (BW) = 75 MHz

Pulse Width (T) = 20 µsec,

So Chirp rate k or the chirp slope BWT 3.75 X 10 Also . . 83.3 10 20 10 1667

So in the algorithm implementation, no. of samples chosen will be in the range of

(-1667/2) to (1667/2) i.e. -833 to +833. These 1667 samples are to be sent in 20 µsec

duration of the chirp. Since the number of samples governs the computation load,

108

because these numbers of sample decide the no. of FFT points so if samples are

reduced, they will reduce the FFT points and also affect the chirp rate accordingly.

On the basis of 1667 samples in 20 µsec, if 512 samples are selected, they will require

only 6.14µsec and the new chirp rate (k) will be

756.14 12.2 10

4.8.2 Azimuth Reference Generation

Another reference chirp signal 2 is generated as a for azimuth expansion.

Typical parameters given (RISAT-2) for azimuth is as follow.

2 4.8.2 Frequency of operation(C- Band SAR of RISAT-2) = 5.350 GHz.

Pulse repetition frequency (PRF) or Azimuth sampling frequency =3000 Hz.

Aircraft tracking velocity (V) = 6.91 km/sec

600 200

= 632 km

Also v=f λ results 3 105.35 10 0.0560

Substituting the above values result chirp rate k =2698.

If the image chosen is 256 x 256 i.e. number of rows are 256 and number of columns

are 256 and no. of samples chosen are 1667 for range expansion then after the range

expansion number of points become (1667+256 number of rows =1923) and to

extract all these samples we need the nearest power of 2 FFT i.e. 2K point (2048

109

points) FFT. Now for extraction of these 1923 samples we apply 2K points FFT in

azimuth direction for azimuth expansion , the number of points become

(1667+256+2K≈ 4K ). So extraction of azimuth expanded image requires at least 4K

point FFT. Thus for the compression purpose we will be using 4K point FFT. In IFFT

we need not to define n points.

So we conclude that for expansion, if 2K point FFT is chosen, we have to opt for 4K

point FFT while going for compression. Same number of points FFT is used for the

reference as well as for the image signal itself. After making final version about FFT,

for range expansion, we multiply FFT of range reference chirp signal (after scaling)

with the FFT of the input image signal (after scaling). While multiplying, since

MATLAB do manipulations in column wise so corner turn memory is carried out for

the input signal before going for MATLAB matrix multiplication.

Scaled Range Expanded image

Figure 4.12: Range Expansion

The same process is carried out for azimuth expansion whose input is range expanded

image after scaling by the maximum value or by 255 (for resultant 8 bits image). This

image is available in column fashion so make corner turn so that original input

arrangement of the image is preserved. The figure 4.13 shows the process of azimuth

expansion.

Range reference FFT

Input signal FFT (rows)

Scaling by maximum value

Scaling by maximum value

Corner Turn

IFFT Scaling

110

SAR Image data

Scaled Range Expanded Image

Figure 4.13: Azimuth Expansion

We have developed image expansion function named ‘expansion’ in MATLAB 7.0 to

expand any simulated image in range and azimuth. The input arguments for this

function are input signal, reference chirp and no. of FFT points while the output is the

expanded image.

4.8.2 Data Scaling

The selected image of the size 256 x 256 is the useful image which is used for further

processing. In order to bring the 256 x 256 image in to 8 bits i.e. to represent the input

SAR data with 8 bits for each channel i.e. in-phase (I) and quadrature phase (Q), we

go for scaling and this scaling factor (typically 1.86e-3 in the present case) confines

the image gray values between –128 to +127.The data thus obtained are hereby

referred as raw data and we have developed ‘image_2_raw’ function for this raw

data conversion and designated the data output as I_8 and Q_8. This data will act as

an input raw data to be processed by the developed algorithms and quantizers for

compression. Below (Figure 4.14) is the noise like image of the raw data.

Azimuth reference FFT

FFT (Column wise)

Scaling by Maximum value

Scaling by the maximum value

Corner Turn

IFFT

111

Figure 4.14: Distribution of Raw data

When we plot the histogram of real and imaginary parts of this raw data, they have

quite a good resemblance with the Gaussian distribution. Thus the data of the

expanded image preserve the shape. The histograms are shown in figure 4.15. If the

quantizer requirement is zero mean and Gaussian distribution of the signal (as in

BAQ it is a prime requirement) then it is made zero by subtracting the signal by its

mean value.

(a)

010203040506070

-128

-109 -90

-71

-52

-33

-14 5 24 43 62 81 100

119

No.

of P

ixel

s

Pixel Value

Raw Data Distribution(Real Part)

Frequency

Range

Azi

mut

h

Unprocessed SAR raw data (256 x 256)

50 100 150 200 250

50

100

150

200

250

112

(b)

Figure 4.15: (a) Histograms of Real and (b) Imaginary Parts of SAR raw data

4.9 Direct Image Compression (Reconstruction)

Now with the raw data output received after image expansion, the next stage is image

compression where we have developed a compression function in MATLAB 7.0

named ‘compression’. In this function we generate two inverse reference chirp

signals one each for range and azimuth directions. The reference inverse chirp

functions are given by

1 4.9.1

and 2 4.9.2

Typical values of the chirp parameters are same as written for expansion.

01020304050607080

-115 -9

7-7

9-6

1-4

3-2

5 -7 11 29 47 65 83 101

119

No.

of P

ixel

s

Pixel Value

Distribution of Raw data(Imaginary Part)

Frequency

113

Scaled Azimuth

Compressed image

Figure 4.16: Azimuth Compression

The same process is carried out for range compression whose input is azimuth

compressed image after scaling by the maximum value or by 255 (for resultant 8 bits

image). This image is available in column fashion so make corner turn so that original

input arrangement of the image is preserved. The figure 4.17 shows the process of

range compression.

Scaled Azimuth Compressed Image

SAR Compressed Image Figure 4.17: Range Compression

In the compression process the FFT of the azimuth inverse reference chirp is first

multiplied with the FFT of the raw data (or quantizer processed data, if quantizer is

used for compression) and then the corner turn process is done by transposing the

Expanded Image FFT (column)

Scaling by maximum value

Corner Turn

Range Reference FFT

FFT (Row wise)

Scaling by the maximum value

Scaling by the maximum value

Corner Turn

IFFT Scaling

IFFT

Scaling by maximum value

Azimuth reference FFT

Corner Turn

114

matrix. After this, inverse FFT is done. Now we go for the same process in the range

direction by multiplying FFT of the inverse range chirp with the FFT of the azimuth-

processed data. Then we again go for corner turn. Inverse FFT of the resultant is done

which gives the final compressed image. The real and imaginary parts of the

compressed image are represented as Proc_I and Proc_Q. The compressed image is

also not of size 256 x 256 because of 1024 point or higher order FFT. The desired

size is extracted and the data is scaled by means of scaling factor (which is 1.84e-3 in

the present case), in order to make every sample restricted to 8 bits i.e. having values

between -128 to and 127. The resultant 8 bits image after compression is shown in

figure 4.18.

Input SAR image (256 x 256) Reconstructed SAR image (Direct Compression)

Figure 4.18: Input and Direct reconstructed Image

4.10 Summary

The chapter has evolved wide discussion on pre-processing of SAR data in image

domain. Various spatial domain filters like median filter, Lee multiplicative filter and

wiener2 filters have been tested on SAR images coming from different satellites like

RADARSAT-2, ERS-2, TerraSAR-X and ENVISAT. The steps involved in

converting the images into raw data and implementation steps used in MATLAB 7.0

have been extensively covered in the chapter. The Pre-processed images are then fed

to the quantizer for further processing like quantization as well as for direct

reconstruction of the signal through compression. Subsequent chapters will explain

the practical procedures involved for the process.