Nature of an Image Flashcards

1
Q

digital RS imagery

A
  • array of scene elements “pixels”
  • based on raster format
  • not a photograph
  • a pixel records EMR from Earth as a digital number
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2
Q

Image

A

a rendition of target features described through the use of EMR

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3
Q

sensors recording EMR determine ____

A

resolution

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4
Q

Tpes of resolution

A
  • spatial
  • spectral
  • temporal
  • radiometric
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5
Q

Pixel location and size:

A

ground footprint

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6
Q

pixel brightness is controlled by:

A

nature of the target

allows creation of spectral signature

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7
Q

levels of pixel brightness determined by:

A

radiometric resolution (bit depth)

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8
Q

types of imaging systems

A
  • frame
  • scanner
  • whiskbroom
  • pushbroom
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9
Q

visible light wavelength range

A

400-700 nm

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10
Q

Spectral resolution

A

number of spectral bands

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11
Q

spatial resolution

A

pixel size or scale

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12
Q

temporal resolution

A

timescale

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13
Q

Multispectral Image

A

image composed of ‘n’ rows and columns of pixels for multiple spectral bands

more than one raster data set or spectral band

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14
Q

what is each data set in a multispectral image

A

a spectral band

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15
Q

multispectral images detect ____ in multiple bands of EMR

A

energy

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16
Q

how to display a colour composite with a multispectral image

A

three bands combine into red, green, and blue

17
Q

Binary Numbers

A

Bit depth (2^n), represent decimal numbers

.# of bits = # of decimal levels

18
Q

how are digital numbers typically stored

A

binary form

19
Q

Digital numbers are a type of what kind of resolution and why

A

radiometric, because the binary number (BIT) is also the amount of grey levels)

20
Q

additive colour

A

RGB on top of each other create white light, and selectively adding RGB creates YMandC

21
Q

Subtractive colour

A

YMC superimposed create black, with RGB in the overlap margins

22
Q

true colour composite

A

RGB guns match RGB bands

23
Q

why digitally process RS data

A
  • capable of more detail and precision than humans
  • humaninterpretation is subjective and not always repeatable
  • computer can better handle large amounts of data
  • can manipulate images with low contrast
24
Q

digital number

A

numerical digit assigned to a pixel

25
compute image histogram
.# of pixels vs DN selected from a region of an actual image
26
DN in a histogram
brightness value represented as anumber (lowest is darkest)
27
why enhance
Increase contrast
28
histogram analysis
examining distribution of pixel values in an image to understand characteristics
29
linear contrast enhancement | min-max stretch
stretch entire histogram linearly based on min and max values
30
percentage contrast enhancement | saturation stretch
zero in on portion of histogram we are interested in and give whole dynamic range to that part
31
pros and cons of percentage stretch
lose variability above and below threshold but gain max contrast in our section ## Footnote outside of our threshold the image is black or white, fully saturated
32
image composite interpretation
composite images by combining multiple bands or channels to enhance specific features