Digital image processing and interpretation for remote sensing study.

1. Introduction to
Digital Image Interpretation

2. What is a Digital Image?
Most remote sensing data can be
represented in 2 interchangeable forms:
Photograph-like imagery
Arrays of digital brightness values

10. Colour Composite Displays
We typically create multispectral image
displays or colour composite images by
showing different image bands in varying
display combinations.

15. True Colour Composites

16. Standard False Colour Composites

17. Colour Composite Images

18. Colour Composite Images

19. General Appearance of Surface Features on
Colour Composite Images
Feature
True Colour
False
Colour
trees and bushes
olive green
red
crops
medium to light green
pink to red
wetland vegetation dark green to black
dark red
water
shades of blue and green blue to black
urban areas
white to light blue
blue to grey
bare soil
white to light grey
blue to grey
Source: U.S. Department of Defense, 1995. Multispectral Users Guide.

20. Digital Image Processing Steps
1.Preprocessing
2.Enhancement
3.Transformation
4.Classification

21. Image Preprocessing
Operations aim to correct distorted or
degraded image data to create a more
faithful representation of the original
scene.
"rectification and restoration"
spatial filtering
radiometric restoration (destriping)
geometric correction

22. Preprocessing functions involve those operations that are
normally required prior to the main data analysis and
extraction of information, and are generally grouped as
 radiometric corrections
 geometric corrections.
Radiometric corrections include correcting the data for
sensor irregularities and unwanted sensor or atmospheric
noise, and converting the data so they accurately represent
the reflected or emitted radiation measured by the sensor.
Geometric corrections include correcting for geometric
distortions due to sensor-Earth geometry variations, and
conversion of the data to real world coordinates (e.g. latitude
and longitude) on the Earth's surface.

23. Various methods of atmospheric correction can be applied ranging
from detailed modeling of the atmospheric conditions during data
acquisition, to simple calculations based solely on the image data.
An example of the latter method is to examine the observed
brightness values (digital numbers), in an area of shadow or for a
very dark object (such as a large clear lake - A) and determine the
minimum value (B). The correction is applied by subtracting the
minimum observed value, determined for each specific band, from
all pixel values in each respective band.

24. Noise in an image may be due to irregularities or errors that
occur in the sensor response and/or data recording and
transmission. Common forms of noise include systematic
striping or banding and dropped lines.
Both of these effects should be corrected before further
enhancement or classification is performed.

25. Image Registration (Geo-referencing)
Registration is the process of superimposing an
image over a map or over another already
registered data. The method of image
registration or “geo-referencing” can be
divided into two types: “image-to-imageregistration” and “image-to-map-registration”.
Selected image data of the Khorat area was
rectified with reference to the 1:50 000 scale
topographic maps (image-to-map-registration).
image-to-map-registration
Further imagery was geo-referenced to this
already registered satellite image using the
image-to-image registration.

27. The geometric registration process involves identifying the image
coordinates (i.e. row, column) of several clearly discernible points,
called ground control points (or GCPs), in the distorted image (A A1 to A4), and matching them to their true positions in ground
coordinates (e.g. latitude, longitude).
The true ground coordinates are typically measured from a map
(B - B1 to B4), either in paper or digital format. This is image-to-map
registration.

28. Geometric registration may also be performed by registering one (or more)
images to another image, instead of to geographic coordinates. This is called
image-to-image registration and is often done prior to performing various
image transformation procedures,
In order to actually geometrically correct the original distorted image, a
procedure called resampling is used to determine the digital values to
place in the new pixel locations of the corrected output image.
3 common methods for resampling:
Nearest neighbour,
Bilinear interpolation,
 Cubic convolution.
Nearest neighbour resampling uses the
digital value from the pixel in the original
image which is nearest to the new pixel
location in the corrected image.
This is the simplest method and does
not alter the original values, but may
result in some pixel values being
duplicated while others are lost. This
method also tends to result in a
disjointed or blocky image appearance.

29. Bilinear interpolation
resampling takes a
weighted average of four
pixels in the original image
nearest to the new pixel
location. The averaging
process alters the original
pixel values and creates
entirely new digital values in
the output image.
This may be undesirable if further processing and analysis,
such as classification based on spectral response, is to be
done. If this is the case, resampling may best be done after the
classification process.

30. Cubic convolution
resampling goes even further
to calculate a distance
weighted average of a block
of sixteen pixels from the
original image which
surround the new output
pixel location. As with
bilinear interpolation, this
method results in completely
new pixel values.
However, these two methods both produce images which have
a much sharper appearance and avoid the blocky appearance
of the nearest neighbour method.

31. Spatial filtering
• Spatial information
– Things close together more alike than things further apart
(spatial auto-correlation)
– Many features of interest have spatial structure such as
edges, shapes, patterns (roads, rivers, coastlines,
irrigation patterns etc. etc.)
• Spatial filters divided into two broad categories
– Feature detection e.g. edges
– Image enhancement e.g. smoothing “speckly” data e.g.
RADAR
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32. Low/high frequency
DN
Gradual change = low
frequency
DN
Rapid change = high
frequency
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33. How do we exploit this?
• Spatial filters highlight or suppress specific
features based on spatial frequency
– Related to texture – rapid changes of DN value =
“rough”, slow changes (or none) = “smooth”
43
49
48
49
51
43
50
65
54
51
12
14
9
9
10
43
49
48
49
51
210
225
199
188
Smooth(ish)
189
Rough(ish)
Darker, horizontal
linear feature
Bright, horizontal
linear feature
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34. Convolution (spatial) filtering
• Construct a “kernel” window (3x3, 5x5, 7x7 etc.) to
enhances/remove these spatial feature
• Compute weighted average of pixels in moving window,
and assigning that average value to centre pixel.
• choice of weights determines how filter affects image
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35. Convolution (spatial) filtering
• Filter moves over all pixels in input, calculate value
of central pixel each time e.g.
43
49
48
49
51
43
50
65
54
51
12
14
9
9
10
43
49
48
49
51
210
225
199
188
189
Input image
??
1/9
1/9
1/9
1/9
1/9
1/9
??
1/9
1/9
??
1/9
filter
Output image
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36. Convolution (spatial) filtering
• For first pixel in output image
– Output DN = 1/9*43 + 1/9*49 + 1/9*48 + 1/9*43 + 1/9*50 +
1/9*65 + 1/9*12 + 1/9*14 + 1/9*9 = 37
– Then move filter one place to right (blue square) and do same
again so output DN = 1/9*(49+48+49+50+65+54+14+9+9) =
38.6
– And again….. DN = 1/9*(48+49+51+65+54+51+9+9+10) = 38.4
• This is mean filter
• Acts to “smooth” or blur image
43
49
48
49
51
43
50
65
54
14
9
9
49
48
49
51
210
225
199
188
189
38.4
10
43
38.6
51
12
37
Output image
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37. Convolution (spatial) filtering
• Mean filter known as low-pass filter i.e. allows low frequency
information to pass through but smooths out higher
frequency (rapidly changing DN values)
– Used to remove high frequency “speckle” from data
• Opposite is high-pass filter
– Used to enhance high frequency information such as
lines and point features while getting rid of low frequency
information
High pass
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38. Convolution (spatial) filtering
• Can also have directional filters
– Used to enhance edge information in a given direction
– Special case of high-pass filter
Vertical edge
enhancement filter
Horizontal edge
enhancement filter
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39. Practical
• Try out various filters of various sizes
• See what effect each has, and construct your
own filters
– High-pass filters used for edge detection
• Often used in machine vision applications (e.g. robotics
and/or industrial applications)
– Directional high-pass filters used to detect
edges of specific orientation
– Low-pass filters used to suppress high freq.
information e.g. to remove “speckle”
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40. Example: low-pass filter
•ERS 1 RADAR image, Norfolk, 18/4/97
•Original (left) and low-pass “smoothed” (right)
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41. Example: high-pass edge
detection
•SPOT image, Norfolk, 18/4/97
•Original (left) and directional high-pass filter (edge detection),
right
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