1. Lecture 2 Photographs and digital mages
Thursday, 7 January 2010
Lecture 2 Photographs and digital mages
Reading assignment:
Ch 1.5
Ch 2.1, 2.5
Ch 3.3 1

2. 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) 2

3. - detectors (film, CCD, etc.)
An imaging system
- scene
- optics
- (scan mirrors)
- focal plane
- detectors (film, CCD, etc.) 3

4. 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 4

5. 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. 5

6. Digital images 200 198 168 199 75 100 167 Histogram
Each pixel is assigned a DN
200
198
168
199 75
100
167 20 10
DN value
Number
Histogram 6

7. Digital images When it is enlarged, a digital photo gets ‘pixilated’
Enlargement 7

8. 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 15 meters
Landsat 5 / ASTER NIR 30 meters
ASTER TIR 90 meters 8

9. 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 = degrees

10. Important spatial properties in images (continued)
° Resolution varies with object
contrast, size, shape
Two point sources
Brightness
Distance
Image profile
Image profile: closer point sources DN DN
Distance
Distance 9

11. Resolution and contrast affect detectability
High contrast
Blurred
Low contrast
Low contrast & blurred 10

12. Noise (measurement error) affects resolution and detectability11

13. Target area affects resolution and detectability
the human eye tends to discriminate based on mean brightness expressed in terms of the standard deviation of the mean (standard error), not the standard deviation of the population 12

14. Recognition of shape is affected by resolving power13

15. Resolution affects identification
Color information only, no spatial information (single pixel, three channels – Blue, Green, & Red)
What can be said in B/W?
What can be said about color alone?
Where does most of the useful information come from? 14

16. Spectral information alone
Spectrum – full “color” information,
no spatial information
Color information,
no spatial information
(single pixel, three channels – B, G, & R) 15

17. Photographs and digital images
We covered:
Photographs and digital images
Structure of brightness elements in images
Detection
Resolution
Signal & noise
Point & extended targets
Next lecture: Spatial data - photointerpretation & photogrammetry 16