1. Radiometric Correction
The radiance measured by any given system over a given object is influenced by:
Changes in scene illumination
Atmospheric conditions
Viewing geometry variations: Greater in the case of airborne data collection than in satellite image acquisition
Instrument response characteristics

2. Radiometric Correction
Sun elevation correction and earth-sun distance correction
Haze compensation

3. Radiometric Correction
Noise correction
electronic noise - both random and periodic
Sun-angle correction
for comparison and mosaic images acquired from different time of the year
Correction for atmospheric scattering
subtract the haze DN values from different bands DN

4. Radiometric Correction
Radiometric Correction addresses:
Sensor and Processing effects
Atmospheric effects
Background
Absolute correction
Relative correction

5. Radiometric Correction
Radiometric corrections may be necessary due to variations in scene illumination and viewing geometry, atmospheric conditions, and sensor noise and response.
Each of these will vary depending on the specific sensor and platform used to acquire the data and the conditions during data acquisition.
Also, it may be desirable to convert and/or calibrate the data to known (absolute) radiation or reflectance units to facilitate comparison between data.

6. Radiometric Correction
Radiometric Correction addresses:
Sensor and Processing effects
Atmospheric effects
Background
Absolute correction
Relative correction
Topographic effects

7. Noise Removal
Image noise is any unwanted disturbance in image data that is due to limitations in the sensing, signal digitization, or data recording process.
Potential source: electronic interference between sensor components
Noise can either degrade or totally mask the true radiometric information content of a digital image
Noise removal usually precedes any subsequent enhancement or classification of the image data

8. Noise Removal
The objective is to restore an image to as close an approximation of the original scene as possible
Line striping or banding  destriping
Line striping occurs due to non-identical detector response
Although the detectors for all satellite sensors are carefully calibrated and matched before the launch of the satellite
With time the response of some detectors may drift to higher or lower levels, resulting in relatively higher or lower values along every sixth line in the image data
Line striping is corrected using histograms per detector

9. Noise Removal
Line drop: Occurs due to recording problems when one of the detectors of the sensor in question either gives wrong data or stops functioning.
The Landsat ETM, for example, has 16 detectors in all its bands, except the thermal band
A loss of one of the detectors would result in every sixteenth scan line being a string of zeros that would plot as a black line on the image
Dropped lines are normally 'corrected' by replacing the line with the pixel values in the line above or below, or with the average of the two.
Detector: Component of a remote sensing system that converts electromagnetic radiation into a recorded signal
Atmospheric Path Radiance Is a term that refers to that component of radiation received by a sensor that did not originate from the target but through scattering in the earth's atmosphere.

10. Noise Removal
SAR-B radar image before and after noise removal

11. Striping-Landsat

12. Partially missing lines-Example

13. Striping
Striping was common in early Landsat MSS data due to variations and drift in the response over time of the six MSS detectors.
The 'drift' was different for each of the six detectors, causing the same brightness to be represented differently by each detector.
The corrective process made a relative correction among the six sensors to bring their apparent values in line with each other

14. Lines
Dropped
Dropped lines occur when there are systems errors which result in missing or defective data along a scan line.
Dropped lines are normally 'corrected' by replacing the line with the pixel values in the line above or below, or with the average of the two.

15. Radiometric Correction
Radiometric Correction addresses:
Sensor and Processing effects
Atmospheric effects
Background
Absolute correction
Relative correction
Topographic effects

16. Radiometric Correction
Direct Illumination

17. Radiometric Correction
Direct Illumination
Scattered Illumination

18. Radiometric Correction
Direct Illumination
Scattered Illumination
Adjacent Reflections
Path Radiance
Irradiance stems from
two sources: (1) directly
reflected sunlight and (2)
diffuse skylight, which is
sunlight that has been
previously scattered
by the atmosphere.

19. Radiometric Correction

20. Radiometric Correction
Rayleigh Scattering
Rayleigh Scattering
Caused by particles much smaller than a wavelength
Declines with the fourth power of the wavelength
Responsible for blue skies, red sunsets
Key element in radiometric correction of images

21. Some form of radiometric correction is required so as to be able to more readily compare images collected by different sensors at different dates or times, or to mosaic multiple images from a single sensor

22. Radiometric Correction
Radiometric Correction addresses:
Sensor and Processing effects
Atmospheric effects
Background
Absolute correction
Relative correction
Topographic effects

23. Absolute Radiometric Correction
Absolute radiometric correction is an analytical approach to deriving reflectance values from raw Digital Numbers (DNs)
1. DN to Radiance
2. Radiance to apparent reflectance
3. Apparent reflectance to ground reflectance

25. Absolute Correction Convert DN to radiance, Lapp
• Sensor dependent
• Lapp=Ai*DN+Bi (Landsat)
• Lapp=DN/Ai (Spot)
• Ai calibration gain, Bi calibration offset
• Which values Ai and Bi to use? Usually both analytical (derived from pre-launch measurements) and empirical (derived from post-launch measurements) exist
MSS shows 8-12% difference ; sensor vs. ground processing (Markham and Barker, 1987)

26. Landsat ETM+ DN to Radiance
L=gain*DN+offset
L= (LMAX-LMIN/255) DN+LMIN
L=Spectral radiance measured (over the spectral bandwidth of the channel)
LMAX=The minimum radiance required to generate the maximum DN (here 255)
LMIN=The spectral radiance corresponding to a DN response of 0
DN=Digital number value recorded
G=Slope of response function (channel gain)
B=Intercept of response function (channel offset)

27. Landsat ETM+ DN to Radiance
(Radiometric response function for an individual TM channel)
Lmax
Gain
L=Spectral radiance
Lmin
Offset
[0,1]
DN=Digital number
255

28. Landsat ETM+ DN to Radiance
Source: ETM+ Science Data Users Handbook

29. Landsat 7 ETM+ DN to Radiance
Lmax=297.5
Gain = (( (-6.2)) /255
Gain
L=Spectral radiance
Lmin=-6.2
Offset=Lmin=-6.2
[0,1]
DN=Digital number
255

30. Atmospheric Effects Lapp=apparent radiance measured by sensor
ρ = reflectance of object
T = atmospheric transmittance
E = irradiance on object, incoming
Lp = path radiance/haze, from the atmosphere and not from the object

31. Sun angle correction
Position of the sun relative to the earth changes depending on time of the day and the day of the year
Solar elevation angle: Time- and location dependent
In the northern hemisphere the solar elevation angle is smaller in winter than in summer
The solar zenith angle is equal to 90 degree minus the solar elevation angle
Irradiance varies with the seasonal changes in solar elevation angle and the changing distance between the earth and sun
Zenith

32. Sun angle correction
An absolute correction involves dividing the DN-value in the image data by the sine of the solar elevation angle
Size of the angle is given in the header of the image data

33. Sun angle correction
Landsat 7 ETM+ color infrared composites acquired with different sun angle.
The left image was acquired with a sun elevation of 37° and right image
with a sun elevation of 42°. The difference in reflectance is clearly shown.
(B) The left image was corrected to meet the right image.

34. Spectral Irradiance & Earth-Sun Distance
An astronomical unit is equivalent to the mean distance between
the earth and the sun, approximately ×106 km
The irradiance from the sun decreases as the square of the earth-
sun distance

35. Haze Reduction
Aerial and satellite images often contain haze. Presence of haze reduces image contrast and makes visual examination of images difficult.
Due to Rayleigh scattering
Particle size responsible for effect smaller than the radiation’s wavelength (e.g. oxygen and nitrogen)
Haze has an additive effect resulting in higher DN values
Scattering is wavelength dependent
Scattering is more pronounced in shorter wavelengths and negligible in the NIR

36. Haze Reduction
One means of haze compensation in multispectral data is to observe the radiance recorded over target areas of zero reflectance
For example, the reflectance of deep clear water is zero in NIR region of the spectrum
Therefore, any signal observed over such an area represents the path radiance
This value can be subtracted from all the pixels in that band

37. Haze Reduction Lapp=apparent radiance at sensor ρ = target reflectance
T = atmospheric transmittance
E = incident solar irradiance
Lp = path radiance/haze

38. Haze Reduction (a) Before haze removal (b) After haze removal
High-altitude normal color air photo of redwood stands and open grass areas
in Redwood Creek Basin, California.

39. Haze-Example Indonesia
(a) Before haze removal (b) After haze removal

40. Haze removal
(a) The aerial image before haze removal (b) The aerial image after haze removal