Basic Explanations about satellite imaging and signal processing with the help of MATLAB.
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4. Signal, System, Signal Processing
● 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
5. Signal, System, Signal Processing
● analog signal
A continuous-time signal with a continuous amplitude
● digital signal
A discrete-time signal with a discrete-value amplitude
6. Signal, System, Signal Processing
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:
7. Signal, System, Signal Processing
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.
8. 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
9. The Application of DSP
DSP application examples
Telecommunications
Multiplexing
Compression
Echo control
Audio Processing
Music
Speech generation
Speech recognition
10. The Application of DSP
DSP application examples
Echo Location
Radar
Sonar
Reflection seismology
Image Processing
Medical
Space
Commercial Imaging Products
11. The Application of DSP
Digital image processing
Deblurring
Noise reduction
Edge detection
12. The sound signal is an example of a 1-D signal
where the independent variable is time
14. 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);
15. 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');
16. Modulation
• Next step is to map each of the symbols onto
constellation (dots on I/Q coordinate).
• >> xmod = qammod(xsym,mlevel);
17. 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');
19. 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);
20. 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
21. 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
23. 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
26. 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
28. Amplitude Modulation
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
29. 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
Amplitude Modulation
31. 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
37. 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!}')
38. 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:
39. 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
44. 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
45. 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
47. A black-and-white image signal is an example of a
2-D signal where the 2 independent variables are the
2 spatial variables.
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48. 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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49. 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.
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50. 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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tyxu
B
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52. 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
53. 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
55. 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
57. 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
58. 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.
62. 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
63. 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
64. 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
65. 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
66. 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.
68. 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')
70. 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)
71. 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
73. 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