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Pixel and Image Characteristics. Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev Sede-Boker Campus 84990, ISRAEL. Pixel (picture element).

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Pixel and image characteristics

Pixel and Image Characteristics

Prof. Arnon Karnieli

The Remote Sensing Laboratory

Jacob Blaustein Institute for Desert Research

Ben-Gurion University of the Negev

Sede-Boker Campus 84990, ISRAEL

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.

Pixel value
Pixel Value

Digital number (DN) =

Gray Level (GL) =

Brightness Value (BV)

Radiance to dn
Radiance to DN

At sensor radiance


Optical system, detectors, electronics

(W m-2 sr-1m-1)

Integer (bit)

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

A row of pixels
A 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.

An image
An 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.

One band
One band

When 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).

Multispectral color composite
Multispectral color composite

Multispectral 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.

True color composite
True Color Composite








False color composite
False Color Composite








Swir color composite
SWIR Color Composite








Pixel and image characteristics

Multispectral 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.


Resolution - The smallest observable (measurable) difference.

  • Resolutions:

  • Spatial

  • Radiometric

  • Spectral

  • Temporal

Spatial resolution
Spatial 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

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.

Field of view fov
Field of View (FOV)

Instantaneous Field of View (IFOV) = Pixel

Field of View (FOV)

Flight direction



Ground projected Instantaneous Field of View (GIFOV)

GIFOV depends on satellite height (H)

Different spatial resolutions
Different spatial resolutions

10 m

20 m

80 m

40 m

Different spatial resolutions1
Different spatial resolutions

1,000 m

300 m

30 m

3 m

Shadow mountain eye project
Shadow Mountain Eye Project

Ninety 61 cm mirrors, 2.25 km across.

Common spectral sensors
Common spectral sensors

Other sensors:

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


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: 1:50,000

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

Radiometric resolution
Radiometric 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

Different gray levels
Different Gray Levels

2 bit - 4 levels

3 bit - 8 levels

1 bit - 2 levels

4 bit - 16 levels

6 bit - 64 levels

8 bit - 256 levels

Cloud shadow
Cloud Shadow

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

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

Dynamic range
Dynamic range

Dynamic Range

Dynamic Range

Spectral resolution
Spectral resolution

  • Spectral 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.)

Multi super hyper ultraspectral
Multi- 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

Signal to noise ratio
Signal 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

Signal to noise ratio1
Signal to Noise Ratio

Laboratory Kaolinite spectrum convolved in various signal to noises

Pixel and image characteristics

Temporal 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”

Pixel and image characteristics


175 km

2800 km


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)

Dn to radiance 1
DN to Radiance (1)

Pixel values (DNs) are scaled to byte values:

Lλ = "gain" * DN + "offset"


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.  

Dn to radiance 2
DN to radiance (2)

Which is also expressed as:


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 = 0Dnmax = the maximum quantized calibrated pixel value (corresponding to Lmaxλ) in DN = 255

Spectral radiance range
Spectral radiance range

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

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

Radiance to reflectance
Radiance to reflectance


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