Geo 213 Final II

Question 1

Principal point

Answer 1

geometric center point on photograph

Question 2

Field of view

Answer 2

angular measurement of how much landscape you can see

Question 3

High oblique

Answer 3

can see horizon

Question 4

Low oblique

Answer 4

~45°

Question 5

Vertical

Answer 5

trying to create “map-like” product

Question 6

High ISO

Answer 6

FASTER shutter speeds
MORE noise (less effective resolution)

Question 7

Low ISO

Answer 7

SLOWER shutter speeds
LESS noise (more resolution)

Question 8

Additive

Answer 8

Red
Green
Blue

Question 9

Subtractive

Answer 9

Yellow
Cyan
Magenta

Question 10

Digital camera

Answer 10

1) solar cell converts photons into electrons
2) matrix of solar cells each capable of producing a voltage
3) voltage converted into digital number 0-255
=intensity of blue, green, red

Question 11

Radiometric resolution

Answer 11

how many bits are available to store brightness
-higher resolution = more shades
=(more memory)

Question 12

Image elements triangle

Answer 12

Primary - tone/color
Secondary - texture, shape, size
Tertiary - pattern, height, shadow
Higher - site, association

Question 13

Small scale

Answer 13

cover MORE area

is LESS detailed

Question 14

Large scale

Answer 14

covers LESS area

is MORE detailed

Question 15

Three types of scale representations

Answer 15

graphic scale
verbal scale
ratio / representative fraction

Question 16

Geospatial intelligence

Answer 16

Imagery
Imagery intelligence
Geospatial information

Question 17

Preprocessing

Answer 17

converting raw imagery into form we can start to make sense of

• geometric correction
• radiometric correction

Question 18

Basic image display

Answer 18

over 210 possible B,G,R color combinations (7 bands)

• THERMAL
• Red = B
• IR = G
• Thermal = R

Question 19

Supervised classification

Answer 19

1) user picks set of land cover class scheme (urban, agriculture…)
2) user identifies examples for each type on image
3) based on collected pixels, user looks at the signatures
4) computer goes through every pixel and matches them with classification
PROBLEMS:
-ambiguity
-different spectral signatures, same class
-mixed pixel problem

Question 20

Unsupervised classification

Answer 20

1) user selects study area + preprocesses
2) user has vague idea of number of types in image
3) computer uses statistical analysis to clump like pixels (creates classifications)
4) user assigns names to these clumps
PROBLEMS:
-can’t differentiate (red on computer = agriculture AND forest)
=may not be useful for your study

Question 21

Color star

Answer 21

FROM TOP, CLOCKWISE
Red
Yellow
Green
Cyan
Blue
Magenta

Question 22

Spatial resolution a function of…

Answer 22

Matrix size
Distance from target
Quality of optics
Quality of electronics

Question 23

Multi

Answer 23

multi-station - use of overlapping photos taken along a given flight line
multi-band - individual bands within a given region of the EM spectrum
multi-spectral - images from various regions of the EM spectrum (thermal, visible, radar…)
multi-temporal - images of same target taken at different times/seasons
multi-stage - image taken from different altitudes