Lecture 2 Photographs and digital mages

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Lecture 2 Photographs and digital mages
Friday, 7 January 2011
Reading assignment Ch 1.5 data acquisition
interpretation Ch 2.1, 2.5 digital imaging Ch
3.3 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. Lamberts 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)

• 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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Tuesdays 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
A photo can be made in color using dye layers
When it is enlarged enough, a photo gets fuzzy
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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.
• A digital number (DN) is assigned to each pixel
  based on the magnitude of the electrical charge.

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
Histogram
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Digital images
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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 Lan
dsat 7 / ASTER VIS 15 meters Landsat 5 /
ASTER NIR 30 meters ASTER TIR 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
Brightness
Distance
Image profile closer point sources
Image profile
DN
DN
Distance
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?
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Spectral information alone
Color information, no spatial information (single
pixel, three channels B, G, R)
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• What was covered in todays 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 Tuesdays lecture
Spatial data - photointerpretation
photogrammetry
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