classification

Question 1

endmember selection

Answer 1

choosing representative spectral signatures to characterize classes, pixel-by-pixel assessment of an image (supervised)

Question 2

k-means

Answer 2

unsupervised classification method that separates data into k clusters based on spectral similarity

Question 3

ISODATA

Answer 3

unsupervised, refines cluster centres and and classifies pixels, removing unnessecary clusters

Question 4

sources of geometric distortion

Answer 4

rotation, projection, resolution, mosaicking

Question 5

image-to-image registration

Answer 5

aligning two images

Question 6

image-to-map

Answer 6

aligning animage with a coordinate system

Question 7

georeferencing

Answer 7

assigning spatial coordinates to pixels

Question 8

GCPs

Answer 8

known locations used for rectification (adjusting an image to a GCS)

Question 9

resampling

Answer 9

adjusting pixel values after geometric correction

Question 10

sensor calibration

Answer 10

adjusting sensor readings to account for variations in sensisitivity over time

Question 11

solar angle corrections

Answer 11

adjusting for variation sin solar illumination

Question 12

terrain correction

Answer 12

compensation for terrain height affecting pixel brightness

Question 13

scatter

Answer 13

changes direction of EMR

Question 14

absorption

Answer 14

absorption of EMR by atmospheric components

Question 15

short wavelengths are ___ and ____ more than long

Answer 15

scattered and absorbed

Question 16

passive sensing

Answer 16

uses natural energy

Question 17

active sensing

Answer 17

sensor creates own energy

Question 18

RADAR

Answer 18

long-wavelength microwaves

Question 19

LIDAR

Answer 19

short-wavelength laser light

Question 20

SONAR

Answer 20

sound, better for water and ground

Question 21

LiDAR

Answer 21

measures distance to a target and intesnity of return, good for topography and DEMs