1. Electro-optical systems Sensor Resolution

2. Outline Electro-optical vs. photographic systems Spatial resolution
Radiometric resolution
Signal-to-noise ratio
Noise equivalent reflectance, radiance, temperature
Spectral Resolution
Temporal Resolution

3. Principles of Detection
Photographic camera/film systems
Digital electro-optical systems

4. Photographic Systems vs. Electro-optical Systems
Both can be used to create images
photography is non-scanning
electro-optical systems can be either scanning or non-scanning
Both record interactions with radiation
films coated with photo-sensitive silver halide emulsions to record an image
electro-optical systems detect radiation through an electrical system
EMR detection capabilities are quite different
film is always passive, uses reflected sunlight, only works in the range of mm
digital systems can be active or passive, can work in all parts of EM spectrum, high spectral resolution is possible, can be non-imaging system (e.g. laser altimeter)

5. The DN that is recorded is proportional to the radiance at the sensor
Digital Number
imaging optics
detectors
electronics
at-sensor
radiance DN
The DN that is recorded is proportional to the radiance at the sensor

6. Digital Raster Imager Format

7. Spatial Resolution
“A measure of the smallest angular or linear separation between two objects that can be resolved by the sensor”. (Jensen, 2000)
Resolving power is the ability to perceive two adjacent objects as being distinct
size
distance
shape
color
contrast with background
sensor characteristics

8. IFOV is a relative measure because it is an angle, not a length
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
It can be measured in radians or degrees
sensor b
IFOV

9. GIFOV
Ground-projected instantaneous field of view (GIFOV) depends on satellite height (H) and the IFOV

10. IKONOS image of Gunnison River Basin, CO
1 kilometer
1 meter resolution
250 meter resolution

11. SPATIAL
RESOLUTION

12. Radiometric Resolution
Number of digital values (“gray levels”) that a sensor can use to express variability of signal (“brightness”) within the data
Determines the information content of the image
The more digital values, the more detail can be expressed

13. Radiometric Resolution
Determined by the number of bits of within which the digital information is encoded
21 = 2 levels (0,1)
22 = 4 levels (0,1,2,3)
28 = 256 levels (0-255)
212 = 4096 levels (0-4095)

14. 2 bit radiometric resolution

15. Dynamic Range
Saturation
Ideal Response (offset for clarity)
Image Brightness
Actual Sensor Response
Dark Current Signal
dark
Scene Brightness
bright

16. Signal Strength
Need enough photons incident on the detector to record a strong signal
Signal strength depends on
Energy flux from the surface
Altitude of the sensor
Location of the spectral bands (e.g. visible, NIR, thermal, etc.)
Spectral bandwidth of the detector
IFOV
Dwell time (more on this next week)

17. Signal-to-Noise Ratio (SNR)
A sensor responds to both signal strength and electronic errors from various sensor components (noise)
SNR = signal-to-noise ratio
signal = the actual energy reaching the detector
noise = random error in the measurement (all systematic noise has been removed)
To be effective, sensor must have high SNR

18. mDN is the mean value of the DNs in the sample population
n is the number of DN values in the sample population

19. 200
201
199
203
202
sensor
50%
DNs from a single detector, measured in a laboratory (pre-launch)
A uniform material of known reflectance
Mean DN = 201
Noise = 1.345
SNR = 201/1.345 = 149

20. Noise Equivalent Radiance Noise Equivalent Reflectance Noise Equivalent Temperature
A measure of the smallest magnitude of signal or of a change in signal that can be detected
Can be expressed in terms of radiance (L) or reflectance (r) or temperature (T)

21. Converting SNR to NE:
Divide the noise value by the DN value recorded over a 100% reflectance target
Since the sensor reads a value of 201 over a 50% reflectance target we assume that it would read 402 over a 100% reflectance target
NE = 1.345402 = = 0.33%
sensor
50%

22. mDN is the mean value of the DNs in the sample population
n is the number of DN values in the sample population

23. Noise Equivalent Radiance Noise Equivalent Reflectance Noise Equivalent Temperature
“Noise equivalent” is a measure of the smallest magnitude of a real change in signal that can be detected
Can be expressed in terms of radiance (L) or reflectance (r) or temperature (T)

24. 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.)

25. Spectral Resolution Determined by: the number of spectral bands
width of each band
Described by the full-width at half-maximum (FWHM)
spectral response function (SRF) of each band

26. Multiple bands:

27. Surface components with very distinct spectral differences can be resolved using broad wavelength ranges

28. Hyperspectral Remote Sensing
Hundreds of bands

29. Temporal Resolution The frequency of data acquisition over an area
Temporal resolution depends on:
the orbital parameters of the satellite
latitude of the target
swath width of the sensor
pointing ability of the sensor

30. Multi-temporal imagery is important for
infrequent observational opportunities (e.g., when clouds often obscure the surface)
short-lived phenomenon (floods, oil spills, etc.)
rapid-response (fires, hurricanes)
detecting changing properties of a feature to distinguish it from otherwise similar features

31. Examples of sensor temporal resolution: SPOT - 26 days (1, 4-5 days with pointing) Landsat - 16 days MODIS - 16 day repeat, 1-2 day coverage
AVHRR – 9 day repeat, daily coverage GOES - 30 minutes
TEMPORAL
RESOLUTION
More about this when we discuss orbits