1

Pixel and Image

Characteristics

Pixel and Image

CharacteristicsProf. Arnon Karnieli

 

The Remote Sensing LaboratoryJacob Blaustein Institute for Desert Research

Ben-Gurion University of the NegevSede-Boker Campus 84990, ISRAEL

Prof. Arnon Karnieli 

The Remote Sensing LaboratoryJacob Blaustein Institute for Desert Research

Ben-Gurion University of the NegevSede-Boker Campus 84990, ISRAEL

2

Pixel (picture element)Pixel (picture element)

A pixel having both spatial and spectral properties. The spatial property defines the "on ground" 2 dimensions. The spectral property defines the intensity of spectral response for a cell in a particular band.

3

Pixel ValuePixel Value

Digital number (DN) =

Gray Level (GL) =

Brightness Value (BV)

4

Radiance to DNRadiance to DN

Optical system, detectors, electronics

At sensor radiance

DN

(W m-2 sr-1 m-1) Integer (bit)

The output (DN) is proportional to the input (at sensor radiance)

5

A row of pixelsA row of pixels

A row of pixels represents a scan line collected as the sensor moves left to right or collected through the use of a linear array of photodetectors.

6

An imageAn image

An image is composed of pixels geographically ordered and adjacent to one another consisting of 'n' pixels in the x direction and ‘m' pixels in the y direction.

7

One bandOne bandWhen only one band of the EM spectrum is sensed, the output device (color monitor) renders the pixels in shades of gray (there is only one data set).

8

Multispectral color compositeMultispectral color compositeMultispectral sensors detect light reflectance in more than one or two bands of the EM spectrum. These bands represent different data. When combined into the red, green, blue guns of a color monitor, they form different colors.

9

True Color CompositeTrue Color Composite

Blue Green Red NIR SWIR1 TIR SWIR2

10

False Color CompositeFalse Color Composite

Blue Green Red NIR SWIR1 TIR SWIR2

11

SWIR Color CompositeSWIR Color Composite

Blue Green Red NIR SWIR1 TIR SWIR2

12

Multispectral imageMultispectral image

A multispectral image is composed of 'n' rows and 'n' columns of pixels in each of three or more spectral bands. There are in reality more than one "data set" which makes up one image.

These different data sets are referred to as spectral bands, bands, or channels.

13

ResolutionsResolutions

Resolutions:

• Spatial

• Radiometric

• Spectral

• Temporal

Resolution - The smallest observable (measurable) difference.

14

Spatial resolutionSpatial resolution

Spatial resolution

• “A measure of the smallest angular or linear separation between two objects that can be resolved by the sensor”

• Resolving power in the ability to perceive two adjacent objects as being distinct

Depends on: - size - distance - shape - color - contrast characteristics - sensor characteristics

15

Instantaneous Field of View (IFOV)Instantaneous Field of View (IFOV)

• Instantaneous field of view (IFOV) is the angular field of view of the sensor, independent of height

• IFOV is a relative measure because it is an angle, not a length.

16

Field of View (FOV)

Field of View (FOV)Field of View (FOV)Instantaneous Field of View (IFOV) = Pixel

Flight direction

17

GIFOVGIFOV

Ground projected Instantaneous Field of View (GIFOV)

GIFOV depends on satellite height (H) H

18

Line-pairs per unit distanceLine-pairs per unit distance

19

Resolution targetResolution target

20

Resolution targetResolution target

2 m 4 m

21

Different spatial resolutionsDifferent spatial resolutions10 m

80 m40 m

20 m

22

Different spatial resolutionsDifferent spatial resolutions1,000 m

30 m 3 m

300 m

23

Contrast and shapeContrast and shape

24

Shadow Mountain Eye ProjectShadow Mountain Eye Project

Ninety 61 cm mirrors, 2.25 km across.

25

Common spectral sensorsCommon spectral sensors

Landsat MSS - 80 m NOAA-AVHRR - 1,100 m Meteosat - 5,000 m

Other sensors:

26

ScaleScale

Scale - mathematical relationship between the size of objects as represented on maps, aerial photographs, or images. Measured as the ratio of distance on an image to the equivalent distance on the ground.

Example: 1:50,0001 cm on the map represents 50,000 cm or 0.5 km on the ground

27

Radiometric resolutionRadiometric resolution

Radiometric resolution

• Number of digital levels that a sensor can use to express variability of brightness within the data

• Determines the information content of the image

• The more levels, the more details can be expressed

• Determined by the number of bits of within which the digital information is encoded

28

Gray levelsGray levels

29

Gray levelsGray levels

30

Gary levels histogramGary levels histogram

31

Different Gray LevelsDifferent Gray Levels

8 bit - 256 levels

2 bit - 4 levels 3 bit - 8 levels

4 bit - 16 levels 6 bit - 64 levels

1 bit - 2 levels

32

Looking within the Shadowed AreaLooking within the Shadowed Area

33

Cloud ShadowCloud Shadow

The features under cloud shadow are recovered by applying a simple contrast and brightness enhancement technique.

Part of the IKONOS (11-bit acquisition level) image is under cloud shadow. It can be recovered due to high radiometric resolution.

34

Dynamic rangeDynamic range

Dynamic RangeDynamic Range

35

Spectral resolutionSpectral resolutionSpectral Resolution

• The width and number of spectral intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive.

• Allows characterization based on geophysical parameters (chemistry, mineralogy, etc.)

36

Multi- Super- Hyper- UltraspectralMulti- Super- Hyper- Ultraspectral

Multispectral: 3 – 10 spectral bands (Landsat-TM, SPOT-HRV, NOAA-AVHRR)

Currently the most common systems

Surperspectral: 10 – 100 spectral bands (MODIS, MERIS, Venµs)

Become more popular in recent years

Hyperspectral: A few hundreds of spectral bands (AVIRIS, Hyperion);

Near-future development

Ultraspectral: A few thousands of spectral bands.

Far-future development

37

Multi- Hyper- UltraspectralMulti- Hyper- Ultraspectral

38

Signal to Noise RatioSignal to Noise Ratio• Sensor responds to a both target brightness (signal) and electronic errors from various sensor components (noise)

• signal = the actual energy reaching the detector

• noise = random error in the measurement (all systematic noise has been removed)

• SNR = signal to noise ratio = Signal/Ratio

• To be effective, sensor must have high SNR

39

Signal to Noise RatioSignal to Noise Ratio

Laboratory Kaolinite spectrum convolved in various signal to noises

40

Signal to Noise RatioSignal to Noise Ratio

41

Signal to Noise RatioSignal to Noise RatioLandsat ALI

42

Hyperspectral conceptHyperspectral concept

43

AVIRISAVIRIS

44

Spectral Cube Spectral Cube

45

1/

Temporal ResolutionTemporal Resolution

Temporal resolution - the frequency of data acquisition over an area

• Depends on:

- the orbital parameters of the satellite

- latitude of the target

- SWATH width of the sensor

- pointing ability of the sensor

• Also called “revisit time”

46

SWATHSWATH

175 km2800 km

47

Tilting CapabilityTilting Capability

48

ImportanceImportance

High temporal resolution is important for:

- infrequent observational opprtunity (e.g., when clouds often obscure the surface)

- short-lived phenomenon (floods, oil spills, dust storms, etc.)

- rapid response (fires, hurricanes)

- detection changes properties of a feature to distinguish it from otherwise similar features (phenology)

49

Summary (1)Summary (1)

50

Summary (2)Summary (2)

51

Temporal vs. Spatial ResolutionTemporal vs. Spatial Resolution

52

DN to Radiance (1)DN to Radiance (1)

Pixel values (DNs) are scaled to byte values:

Lλ = "gain" * DN + "offset"

where:

Lλ= Spectral radiance at the sensor’s aperture in

watts/(meter2*ster*µm)

"gain" = Rescaled gain in watts/(meter2*ster*µm)

"offset"= Rescaled bias in watts/(meter2*ster*µm)

“gain” and “offset” values are provided with the image.  

53

DN to radiance (2)DN to radiance (2)

max minmin min

max min

L LL L DN DN

DN DN

Which is also expressed as:

Where:

Lminλ= the spectral radiance that is scaled to

DNmin in watts/(m2 * ster * µm)

Lmaxλ= the spectral radiance that is scaled to

DNmax in watts/(m2 * ster * µm)

DNmin = the minimum quantized calibrated pixel value (corresponding to Lminλ) in DN = 0

Dnmax = the maximum quantized calibrated pixel value (corresponding to Lmaxλ) in DN = 255

54

Spectral radiance rangeSpectral radiance range

Wavelength Lmin Lmax( µm)

1 0.45-0.52 -6.2 191.62 0.52-0.60 -6.4 196.53 0.63-0.69 -5 152.94 0.76-0.90 -5.1 157.45 1.55-1.75 -1 31.067 2.08-2.35 -0.35 10.8

Band Number

Lmin, Lmax = radiance in w m-2st-1m-1

Example for the Landsat ETM+ sensor, high gain, after July 1, 2000

55

Radiance to reflectanceRadiance to reflectance2

cos s

L d

ESUN

Where:

p =   Unitless planetary reflectance

L=   Spectral radiance at the sensor's aperture

d =   Earth-Sun distance in astronomical units from nautical handbook

ESUN =   Mean solar exoatmospheric irradiances

s =   Solar zenith angle in degrees

56

TablesTables

Julian Day Distance

Julian Day Distance

Julian Day Distance

Julian Day Distance

Julian Day Distance

1 0.9832 74 0.9945 152 1.014 227 1.0128 305 0.992515 0.9836 91 0.9993 166 1.0158 242 1.0092 319 0.989232 0.9853 106 1.0033 182 1.0167 258 1.0057 335 0.98646 0.9878 121 1.0076 196 1.0165 274 1.0011 349 0.984360 0.9909 135 1.0109 213 1.0149 288 0.9972 365 0.9833

Earth-Sun Distance in Astronomical Units

Band watts/(meter squared * µm)1 19692 18403 15514 10445 225.77 82.07

Solar Spectral Irradiances