Signal and image processing on satellite communication using MATLAB

SIGNAL AND IMAGE
PROCESSING ON SATELLITE
COMMUNICATIONS USING
MATLAB
Presented By
K. KALPANA JESI
EMBEDDED PLUS SOLUTIONS,
TRICHY

Copyright © 2005. Shi Ping CUC
Signal, System, Signal Processing
 Signal
A signal is a function of independent variables such as
time, distance, position, temperature and pressure
Signals play an important role in our daily life.
Most signals we encounter are generated naturally.
However, a signal can also be generated synthetically
or by a computer.

● one dimensional (1-D) signal
A function of a single independent variable
● multidimensional (M-D) signal
A function of more than one independent variables
sound Image Video

● analog signal
A continuous-time signal with a continuous amplitude
● digital signal
A discrete-time signal with a discrete-value amplitude

 System
A system is any process that produces an output signal
in response to an input signal.
Depending on the types of the signal processed, we can
classify the systems as follows:

 Signal Processing
A signal carries information !
The objective of signal processing:
To extract, enhance, store and transmit the useful
information carried by the signal.
Digital signal processing:
To implement the signal processing by a digital means.

The Application of DSP
 Signal Analysis
 Measurement of signal properties
 Spectrum (frequency/phase) analysis
 Target detection, verification, recognition
 Signal Filtering
 Signal-in-signal-out, filter
 Removal of noise/interference
 Separation of frequency bands
The main tasks of DSP

DSP application examples
 Telecommunications
 Multiplexing
 Compression
 Echo control
 Audio Processing
 Music
 Speech generation
 Speech recognition

DSP application examples
 Echo Location
 Radar
 Sonar
 Reflection seismology
 Image Processing
 Medical
 Space
 Commercial Imaging Products

Digital image processing
Deblurring
Noise reduction
Edge detection

The sound signal is an example of a 1-D signal
where the independent variable is time

Block processing for signal
communication

Creating Random Bit
Stream
• This is the first step of
transmission. In real
communication this can be a
meaningful data like file, space
signal etc..
• But in most of simulations or
even in real life test use a
sequence of random numbers
(random bits) as an input data.
• >> x = randi([0 1],N,1);

Converting Bit Stream
into Symbol Stream
• Now we are converting a sequence of bits into a
sequence of symbols.
• >>xsym =bi2de(reshape(x,k,length(x)/k).','left-msb');

Modulation
• Next step is to map each of the symbols onto
constellation (dots on I/Q coordinate).
• >> xmod = qammod(xsym,mlevel);

Channel - Adding Noise
Once the signal gets into
the space (channel), a
variety of noise is added.
• code
>>SNR = 5;
>>Tx_awgn =
awgn(Tx_x,SNR,'measure
d');

Discrete Signals
Time base: t = [0.0 0.1 0.2 0.3];
Signal data: x = [1.0 3.2 2.0 8.5];
Creating vectors in MATLAB:
>> t = [0.0 0.1 0.2 0.3];
>> t = 0:0.1:0.3;
>> t = linspace(0, 0.3, 4);

Modeling Noise with
Random Data
2-20
>> un = -5+10*rand(1,1e6);
>> hist(un,100)
>> gn = 10+5*randn(1,1e6);
>> hist(gn,100)
Uniform Gaussian

Adding Noise to a Signal
2-21
noisy signal = signal + noise
>> y1 = x + rand(size(x)) uniform noise
>> y2 = x + randn(size(x)) Gaussian noise

Simulate a Source
Source
Channel
Destination Demodulator
Modulator
 Produces message signal … e.g. a simple Sine wave

Generate message signal (simple sine wave)
 Define time instants (1000 sample points)
tmin = 0; tmax = 10^(-3); step = (tmax-tmin)/1000;
t = tmin:step:tmax;
 Define amplitude and frequency (initial phase is zero)
Vm = 1; % Amplitude
fm = 2*10^3; % Frequency
 Construct the Signal
m = Vm*sin(2*pi*fm*t);
 View the Signal
plot(t,m,'r');
Simulate a Source
   tfVtm mm  2sin

Simulate Modulation
Source
Channel
Modulator
 Built-in functions are available (ammod, amdemod
etc.)

Amplitude Modulation
Simulate with built-in functions
fs = 8000; % Sampling rate is 8000 samples per second
fc = 300; % Carrier frequency in Hz
t = [0:0.1*fs]'/fs; % Sampling times for 0.1 second
m = sin(20*pi*t); % Representation of the signal
v = ammod(m,fc,fs); % Modulate m to produce v
figure(1)
subplot(2,1,1); plot(t,m); % Plot m on top
subplot(2,1,2); plot(t,v); % Plot v below
mr = amdemod(v,fc,fs); % Demodulate v to produce m
figure(2);
subplot(2,1,1); plot(t,m); % Plot m on top
subplot(2,1,2); plot(t,mr); % Plot mr below

Continued ….
 Modulate the Signal,
v = (1+m/Vc).*c; % DSB-FC modulation
 View Modulated Wave
plot(t,v); % Modulated Wave
hold on;
plot(t,Vc*(1+m/Vc),'r:'); % Upper Envelope
hold on;
plot(t,-Vc*(1+m/Vc),'r:'); % Lower Envelope
hold off ;
     tftf
V
V
Vtv cm
c
m
c 





 2sin2sin1

Complete MATLAB Script
clear all; close all; clc;
tmin = 0; tmax = 10^(-3); step = (tmax-tmin)/1000;
t = tmin:step:tmax; % Time
Vm = 1; Vc = 2; % Amplitude
fm = 2*10^3; fc = 10^4; % Frequency
m = Vm*sin(2*pi*fm*t); % Message
c = Vc*sin(2*pi*fc*t); % Carrier
v = (1+m/Vc).*c; % Modulated Wave
plot(t,v); hold on;
plot(t,Vc*(1+m/Vc),'r:'); hold on; % Upper Envelope
plot(t,-Vc*(1+m/Vc),'r:'); hold off % Lower Envelope

Ideal Demodulation of DSB-SC
clear all; close all; clc;
fs = 10^5; N = 10^5;
t = 1/fs:1/fs:N/fs;
fm = 2; fc = 10^3;
m = sin(2*pi*fm*t);
c = sin(2*pi*fc*t);
v = m.*c;
r = zeros(1,N); n =f s/fc;
for k = 1:fc
mr((k-1)*n+1:k*n) = 2*v((k-1)*n+1:k*n)*c((k-1)*n+1:k*n)'/n;
end
figure(1)
subplot(2,1,1); plot(t,m);
subplot(2,1,2); plot(t,mr);
Demodulation

Analog Communication Systems
Source
Channel
Modulator
 Introduces noise … Additive White Gaussian Noise

Simulate Channel
Introducing AWGN
fs = 10^5; N = 10^5;
t = 1/fs:1/fs:N/fs;
fm = 2; fc = 10^3;
m = sin(2*pi*fm*t);
c = sin(2*pi*fc*t);
v = m.*c;
SNRdB = 10; SNR = 10^(SNRdB/10);
vn = var(v)/SNR;
n = sqrt(vn)*randn(1,N);
v = v + n;
r=zeros(1,N); n=fs/fc;
for k=1:fc
mr((k-1)*n+1:k*n)=2*v((k-1)*n+1:k*n)*c((k-1)*n+1:k*n)'/n;
end
figure(1)
subplot(2,1,1); plot(t,m);
subplot(2,1,2); plot(t,mr); axis([0 1 -1 1])

Plot Power: Contour & 3-D Mesh
>> t = 0:pi/25:pi;
>> [x,y,z] = cylinder(4*cos(t));
>> subplot(2,1,1)
>> contour(y)
>> subplot(2,1,2)
>> mesh(x,y,z)
>> xlabel('x')
>> ylabel('this is the y axis')
>> text(1,-2,0.5,...
'it{Note the gap!}')

Mesh Plots
>> figure;
>> [X,Y] = meshgrid(-16:1.0:16);
>> Z = sqrt(X.^2 + Y.^2 + 5000);
>> mesh(Z)
•mesh(Z) generates a wireframe view of matrix Z,
where Z(i,j) define the height of a surface over the
rectangular x-y grid:

Surface Plots
•surf(Z) generates a colored faceted 3-D view of the surface.
– By default, the faces are quadrilaterals, each of constant
color, with black mesh lines
– The shading command allows you to control the view
>> figure(2);
>> [X,Y] = meshgrid(-
16:1.0:16);
>> Z = sqrt(X.^2 + Y.^2 +
5000);
>> surf(Z)
>> shading flat
>> shading interp
Default: shading faceted

Surface Plots: Colormaps
>> colormap hot
>> colormap gray
>> colormap cool
>> colormap pink

More Surface Plots
>> meshc(Z)
>> meshz(Z)
>> surfl(Z)
>> pcolor(Z)

More Contour Plots
>> Z = peaks;
>> [C, h] = contour(Z, 10);
>> clabel(C, h);
>> title('Labeled Contour')
>> Z = peaks;
>> [C, h] = contourf(Z, 10);
>> title('Filled Contour')
>>

pixel
The cells are sensed one after another along the line.
In the sensor, each cell is associated with a pixel that is
tied to a microelectronic detector
Pixel is a short abbreviation for Picture Element
a pixel being a single point in a graphic image
Each pixel is characterized
by some single value of radiation
(e.g., reflectance) impinging on
a detector that is converted by
the photoelectric effect into electrons
2Q - see handout, Q is bit of each pixel

Image Processing
• Image Processing
The techniques fall into three broad categories:
o Image Restoration and Rectification
o Image Enhancement
o Image Classification
• There are a variety of CASI methods:
Contrast stretching, Band transformation,
Principal Component Analysis, Edge
Enhancement, Pattern Recognition

Contents
• Images Basic
• Satellite Images
• Image Acquisition
• Image Enhancement
• Image Conversion
• Image Segmentation

A black-and-white image signal is an example of a
2-D signal where the 2 independent variables are the
2 spatial variables.
),( yxI

A color image signal is a 3-channel signal composed of three
2-D signals representing the three primary color: red, green
and blue (RGB)
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


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



),(
),(
),(
),(
yxI
yxI
yxI
yxu
G
G
R

A black-and-white video signal is an example of a 3-D signal
where the 3 independent variables are the 2 spatial variables
and the time variable.
),,( tyxI

A color video signal is a 3-channel signal composed of three
3-D signals representing the three primary color: red, green
and blue (RGB)
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
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



),,(
),,(
),,(
),,(
tyxI
tyxI
tyxI
tyxu
B
G
R

Satellite Images
•
• Infrared
• Image
Water Vapor Visible
Image Image

Online Reading
•
Image Type Pixel Value Color Levels
8-bit image 28 = 256 0-255
16-bit image 216 = 65536 0-65535
24-bit image 224 = 16777216 0-16777215

Data visualization
The images that we view are visual representations
of the digital output from the sensor
 8-bit gray shade image is the case when the
sensor output is converted to one of 256 gray
shades (0 to 255)
 24-bit color does the same except in shades or
red, green, and blue

2-bit Image
(4 grey levels)
8-bit Image
(256 grey levels)

Reading Satellite Image
• multibandread - for .lan file
contains a 7-band 512-by-512 Landsat image
• imread - for SAR images
This images are represented as row X column of R,G,B

Image Restoration
• Image Restoration: most recorded images are subject
to distortion due to noise which degrades the image.
Two of the more common errors that occur in multi-
spectral imagery are striping (or banding) and line
dropouts

Enhancement
• Imadjust - Adjust image intensity values
Stretchlim
Find limits to contrast stretch image
• Histeq - Improves by histogram equalisation
The transformation b = T(a) to map the gray levels in X (or
the colormap) to their new values.
• Adapthisteq - operates on small regions in the
image, called tiles, rather than the entire image.

Image Conversion
• Convert to single Plane- rgb2gray
• Convert to another spectral resolution
HSI conversion
SAR conversion

Separated Bands of
Satellite image

Spatial Filtering
• Spatial filters are designed to highlight or suppress
features in an image based on their spatial frequency.
• Spatial filters are used to suppress 'noise' in an image,
or to highlight specific image characteristics.
Low-pass Filters
High-pass Filters
Directional Filters

Spatial Filtering
• Low-pass Filters:
These are used to emphasize large homogenous areas of similar
tone and reduce the smaller detail. Low frequency areas are
retained in the image resulting in a smoother appearance to the
image.
Linear Stretched Image Low-pass Filter Image

Spatial Filtering
• High-pass Filters: allow high frequency areas
to pass with the resulting image having greater
detail resulting in a sharpened image
Hi-pass FilterLinear Contrast Stretch

Spatial Filtering
• Directional Filters:
are designed to enhance linear features such as roads, streams,
faults, etc.The filters can be designed to enhance features which are oriented
in specific directions, making these useful for radar imagery and for
geological applications. Directional filters are also known as edge detection
filters.
Edge Detection
Lakes & Streams
Edge Detection
Fractures & Shoreline

Image Classification
• In classifying features in an image we use the elements
of visual interpretation
to identify homogeneous groups of pixels which
represent various features or land cover classes of
interest.

Image Segmentation
• NIR band (displayed as red) with the visible red band
(displayed as green)
NIR = im2single(CIR(:,:,1));red = im2single(CIR(:,:,2));
figure;subplot(121);
imshow(red);title('Visible Red Band')
Subplot(122),imshow(NIR);
title('Near Infrared Band')

Data Visualization
Ability to quickly discern features is improved by using 3-band color mixes
Image below assigns blue to band 2, green to band 4, and red to band 7
Vegetation is green
Surface water is blue
Playa is gray and white
(Playas are dry lakebeds)

Multispectral display - CIR
• Visualize spectral content with 3-
band color composites
• Example: color infrared (CIR)
– red channel assigned to near IR
sensor band
– green channel assigned to red
sensor band
– blue channel assigned to green
sensor band
• vegetation appears red,
soil appears yellow - grey,
water appears blue - black

File formats
File formats play an important role in that many are
automatically recognized in image processing packages
• GeoTIFF is a variant of TIFF that includes
geolocation information in header
• HDF or Hierarchical Data Format is a self-
documenting format
All metadata needed to read image file
contained within the image file
• NITF or National Imagery Transmission Format
Department of Defense

Signal and image processing on satellite communication using MATLAB

Signal and image processing on satellite communication using MATLAB

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