Transcript No Slide Title

Friday, 7 January 2011
Lecture 2
Photographs and digital mages
Reading assignment:
Ch 1.5
Ch 2.1, 2.5
Ch 3.3
data acquisition & interpretation
digital imaging
scale
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What was covered in the previous lecture
•
•
LECTURES
Jan 05 1. Intro
previous
Jan 07 2. Images
today
Jan 12 3. Photointerpretation
Jan 14 4. Color theory
Jan 19 5. Radiative transfer
Jan 21 6. Atmospheric scattering
Jan 26 7. Lambert’s Law
Jan 28 8. Volume interactions
Feb 02 9. Spectroscopy
Feb 04 10. Satellites & Review
Feb 09 11. Midterm
Feb 11 12. Image processing
Feb 16 13. Spectral mixture analysis
Feb 18 14. Classification
Feb 23 15. Radar & Lidar
Feb 25 16. Thermal infrared
Mar 02 17. Mars spectroscopy (Matt Smith)
Mar 04 18. Forest remote sensing (Van Kane)
Mar 09 19. Thermal modeling (Iryna Danilina)
Mar 11 20. Review
Mar 16 21. Final Exam
Introduction
•Remote sensing
•Images, maps, & pictures
•Images and spectra
•Time series images
•Geospatial analysis framework
•Useful parameters and units
•The spectrum
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Tuesday’s lecture was an introduction to remote sensing
We discussed:
what remote sensing was
something about maps, images, and spectra
time-series images - movies
what was to be covered in this class
Today we discuss imaging systems and some of their characteristics
Specialized definitions:
scene the real-world target or landscape
image a projection of the scene onto the focal plane of a camera
picture some kind of representation of the image (e.g., hard copy)
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An imaging system
- scene
- optics
- (scan mirrors)
- focal plane
- detectors (film, CCD, etc.)
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Photographs
Photographs utilize concentrations
of opaque grains
to represent brightnesses
When it is enlarged enough,
a photo gets fuzzy
A photo can be made in
color using dye layers
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Digital Images
A Charged Couple Device replaces the photographic film.
CCD




silicon wafer
solid-state electronic component
array of individual light-sensitive cells
each = picture element (“pixel”)
Each CCD cell converts light energy into
electrons.
In the case of digital cameras:
A digital number (“DN”) is assigned to each pixel
based on the magnitude of the electrical charge.
Each pixel on the image sensor has red,
green, and blue filters intermingled
across the cells in patterns designed to
yield sharper images and truer colors.
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Digital images
Each pixel is assigned a DN
0
0
0 200 100
100 0 198 75
0
198 0
75 168 75 168
0
Histogram
0 198
198 0
0
20
0 100 75
0 167 168 199
Number
0
10
0
0
100
200
DN value
250
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Digital images
When it is enlarged,
a digital photo gets ‘pixilated’
Enlargement
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Important spatial properties in images
° Field of view (“FOV”)
- Distance across the image (angular or linear)
° Pixel size
- Instantaneous Field of view (“IFOV”)
Size in meters or is related to angular IFOV and height above ground
ex: 2.5 milliradian, at 1000 m above the terrain
1000 m * (2.5 * 10-3 rad) = 2.5 m
Each pixel represents a ~square area in the scene that is a
measure of the sensor's ability to resolve objects
Examples:
Landsat 7 / ASTER VIS
Landsat 5 / ASTER NIR
ASTER TIR
15 meters
30 meters
90 meters
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Radians defined
• Radian is a measure of angle, like degrees
• The circumference of a circle = 2 p r, where
r is its radius.
• There are 2 p radians in a circle and 360
degrees
• A radian is therefore a little over 57 degrees
• 2.5 milliradians = 0.143 degrees
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Important spatial properties in images (continued)
° Resolution varies with object
contrast, size, shape
TWO POINT SOURCES
1.2
Two point
sources
BRIGHTNESS
Brightness
1
0.8
0.6
0.4
0.2
0
0
10
20
30
Distance
40
50
DISTANCE
IMAGE PROFILE
IMAGE PROFILE
0.5
0.6
0.45
Image
profile
0.4
0.3
0.4
DN
SIGNAL
SIGNAL
DN
0.35
Image profile:
closer point
sources
0.5
0.25
0.2
0.15
0.3
0.2
0.1
0.1
0.05
0
0
0
10
20
30
Distance
DISTANCE
40
50
0
10
20
30
Distance
40
50
DISTANCE
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Resolution, contrast & ‘noise’ affect detectability
High contrast
Low contrast & blurred
Low signal/noise
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Large targets are more easily detected
Blurred, no measurement error
with ‘noise’
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Recognition of shape is affected by resolving power
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Resolution affects identification
What can be said in B/W?
What can be said about color alone?
Where does most of the useful information come from?
Color information only, no spatial information
(single pixel, three channels – Blue, Green, &
Red)
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Spectral information alone
Color information,
no spatial information
(single pixel, three
channels – B, G, & R)
Spectrum – full “color” information,
no spatial information
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What was covered in today’s lecture?
•Photographs and digital images
•Structure of brightness elements in images
•Detection
•Resolution
•Signal & noise
•Point & extended targets
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What will be covered in Tuesday’s lecture
Spatial data - photointerpretation &
photogrammetry
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