Digital images are composed of pixels arranged in rows and columns, with each pixel assigned a numerical value representing brightness or color. Remote sensing uses sensors on satellites and other platforms to record digital images without direct contact. These images can then be analyzed and manipulated in various ways, such as assigning pseudo-colors, enhancing contrast, and measuring pixel values to learn quantitative information about the objects or areas represented in the images.

1. Applying Pixel Values to
Digital Images
Guilford County SciVis
V202.02

2. Digital Images Gather Data by Remote
Sensing
 Remote Sensing is the
process of gathering
information without
touching it.
 Satellites use a
number of different
sensors (IR, UV,
Visible light, Radio) to
record information

3. Digital Images Gather Data by Remote
Sensing
 Example of remote sensing
include:
 The Human eye gathering
light.
 Bats sensing their
environment by emitting
sound waves and listening
for the reflected echo.
 Rattlesnakes detecting
heat radiation of small
animals
 Sounds waves used in
medical Imaging (MRI)
 X-rays used to detect to
detect broken bone

4. What are Digital Images?
 Digital images are
composed of pixels
arranged in rows and
columns.
 Each pixel carries a
numerical value or digital
number (DN).
 Colors or shades of gray
(brightness) are
assigned to each DN.

5. What are Digital Images?
 A LUT (Look-Up table)
is used to show the
scale relationship
between each pixel’s
DN and its assigned
color or gray brightness
value.
 Changing the LUT scale
controls the appearance
of the image.

6. What are Digital Images?
 To digitize a photo, we superimpose a grid over
the image and use the intersection of a row with
a column to locate objects (similar to longitude
and latitude).
 The brightness value at each X and Y location
can be recorded by the satellite scanner
(detector). The brightness value or DN is the Z-
value associated with a picture element or pixel.
 By reducing the size or spacing of the grid, we
can digitize at a finer and finer spatial resolution.

7. What are Digital Images?
 Digital data can be manipulated in a number of
ways
 Colors can be assigned to an image using the
LUT (Pseudo-color images). This process helps
visualize specific DN values. Use of colors
includes:
 Making clouds visible in weather maps
 Coloring malignant cells a bright color to
stand out in an image
 Separating important data from the rest of
the image
 Separating colder blue
areas and warmer red
areas in a weather map

8. Multi-spectral Remote Sensing
 Recording energy in the red, green, blue, IR, UV,
or other parts of the electromagnetic spectrum.
 Collected data can be qualitative or quantitative.
 TIR (Thermal Infrared) sensing exploits the fact
that everything above absolute zero (0 K, -273 C,
-459 F) emits radiation in the infrared range. For
example, infrared weather satellite can sense
temperature.

9. Multi-spectral Remote Sensing
 Multi-spectral remote
sensing measures the
amount of energy reflected
in discrete bands that
correspond to specific
colors.
 Scientists can obtain
information from these
wavelengths (color).
 For example, scientists can
pick out the range of color
of marijuana plants grown
in North Carolina.

10. Contrast and Brightness Levels
 The BRIGHTNESS is the intensity of white
in an image.
 The CONTRAST is the difference between
the lightness and darkness of an image.

11. Histograms in Area Renderings
 Histograms are displayed as a
bar graph with each shade of
gray represented by one of
the vertical lines. The height
of the bar displays the number
of pixels.
 By mathematical stretching
the range of pixels, features
can be seen which are not
apparent. Areas such as rivers
streams, and lakes are
enhanced and are more easily
viewable.

12. Measuring in Area Renderings
 The portion of the earth
is dependent on the
height of the satellite.
Most weather satellites
use the scale of 16
square miles equals 1
pixel. Programs like
Scion Image allow you
to set these scales.
 The curve of the earth
may add variation to
these renderings.

13. Density Slicing
 Density slicing allows you to
highlight a range of pixel values in
the LUT.
 The highlighted pixels help
visualize areas of interest.
 Density slicing is part of “particle
analysis” where you let the
computer locate an area of interest
(a lake for example) and then the
computer will analyze the slice
(area, height, temperature, etc.)

14. Particle Analysis
 Particle analysis allows one
to highlight specific pixel
values and take
measurements (i.e.
calculating the area and
perimeter of a massive cloud
or the size of a tumor).
 Combining Set Scale,
Density Slicing, and Particle
Analysis is very useful to
isolate and measure specific
parts of a digital image.

15. Density Calibration
 Density calibrating an image will mathematically
relate the values of one pixel to other pixels.
 If you know the elevation of a particular
mountain, you can select those pixels and set the
scale. The relative elevation of the other areas
can then be estimated.
 Density calibration is based on making
predictions from a line graph (linear regression).

16. Digital Elevation Models (DEM’s)
 DEM’s are images
where DN (digital
number) or grayscale
values represent
elevation.
 A digital image can be
converted into a relief
map.
 In Scion Image, we
use Surface Plot to
produce a relief map.

17. Elevation Calibration (Calibrated
DEM’s)
 Take the data from a
DEM to apply known
elevation to calculate
other elevations.
 Mapping out the sea
surface floor is an
example of using this
method.

18. Animation
 Multiple images of an area can be opened at the
same time. (Images of a hurricane taken every
hour).
 Images are put into a “stack” which works very
much like a “flip book”. Useful for showing the
movements of storm patterns.

19. The End