Post on 15-Jan-2016
Lecture 2 Photographs and digital mages
Friday, 7 January 2011
Reading assignment:
Ch 1.5 data acquisition & interpretationCh 2.1, 2.5 digital imagingCh 3.3 scale
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What was covered in the previous lecture
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LECTURES• Jan 05 1. Intro previous• Jan 07 2. Images today
Jan 12 3. PhotointerpretationJan 14 4. Color theoryJan 19 5. Radiative transferJan 21 6. Atmospheric scatteringJan 26 7. Lambert’s LawJan 28 8. Volume interactionsFeb 02 9. SpectroscopyFeb 04 10. Satellites & ReviewFeb 09 11. MidtermFeb 11 12. Image processingFeb 16 13. Spectral mixture analysisFeb 18 14. ClassificationFeb 23 15. Radar & LidarFeb 25 16. Thermal infraredMar 02 17. Mars spectroscopy (Matt Smith)Mar 04 18. Forest remote sensing (Van Kane)Mar 09 19. Thermal modeling (Iryna Danilina)Mar 11 20. ReviewMar 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
Tuesday’s lecture was an introduction to remote sensingWe discussed:
what remote sensing wassomething about maps, images, and spectratime-series images - movieswhat 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
When it is enlarged enough, a photo gets fuzzy
A photo can be made incolor using dye layers
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Photographs utilize concentrations of opaque grains to represent brightnesses
Digital Images
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.
A digital number (“DN”) is assigned to each pixel based on the magnitude of the electrical charge.
A Charged Couple Device replaces the photographic film.
In the case of digital cameras: 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
0 200
198
168
199
75
100
100
100
75 75
75
168167
168
0 0 0
0
0
0
0 0
0
0
0
0
198
198
198
0 100 200 250
20
10
0
DN value
Num
ber
Histogram
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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 groundex: 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 15 metersLandsat 5 / ASTER NIR 30 metersASTER TIR 90 meters
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Radians defined
• Radian is a measure of angle, like degrees
• The circumference of a circle = 2 r, where r is its radius.
• There are 2 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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IMAGE PROFILE
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 10 20 30 40 50
DISTANCE
SIG
NA
L
Important spatial properties in images (continued)
TWO POINT SOURCES
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50
DISTANCE
BR
IGH
TN
ESS
IMAGE PROFILE
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50
DISTANCE
SIG
NA
L
Distance
Distance
DN
Bri
ghtn
ess
Two point sources
Image profile
Image profile: closer point sources
Distance
DN
° Resolution varies with object contrast, size, shape
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High contrast
Resolution, contrast & ‘noise’ affect detectability
Low contrast & blurred
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Low signal/noise
Large targets are more easily detected
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Blurred, no measurement error with ‘noise’
Recognition of shape is affected by resolving power
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Color information only, no spatial information (single pixel, three channels – Blue, , Green, & , & Red)
Resolution affects identification
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What can be said in B/W? What can be said about color alone?Where does most of the useful information come from?
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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Spatial data - photointerpretation & photogrammetry
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What will be covered in Tuesday’s lecture