1. Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI …we will begin shortly

2. Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI
The phone lines will be muted for sound quality.
Please direct questions to the Chat window. My colleague will be available to answer any questions.
The presentation will be recorded and posted to the ITTVIS website

3. Survey!
Do you work on a Windows, Mac, or Unix machine?

4. Topics to Cover Concepts in Remote Sensing
Why calibration and atmospheric correction are important data pre-processing tasks
Basic tools in ENVI to account for general atmospheric effects
Advanced tools in ENVI for robust atmospheric correction
The difference between raw data, radiance, and reflectance data
Applications that rely on atmospherically corrected and calibrated data

5. Sun - Sensor Pathway Sensor Path Radiance (scattered light)
Solar Irradiance
Absorbed by
atmospheric gases
Radiance (reflected and emitted energy)
absorbed

6. Solar Spectrum Spectral Irradiance (W/m2 mm) Wavelength (mm) 0.25 0.20
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
0.05
0.10
0.15
0.20
0.25
Wavelength (mm)
Spectral Irradiance (W/m2 mm)
Blackbody at 5900K
Solar irradiance outside atmosphere
Solar irradiance at sea level O3
H2O
02, H2O
H2O, CO2
(From Valley, 1965)

7. The Electromagnetic Spectrum
NEAR-
INFRARED
(NIR)
SHORT WAVE
(SWIR)
1 nm
10 nm
100 nm
1 mm
10 mm
1 cm
10 cm
1 m
10 m
100 m
1 km
10 km
400.0
700.0
1000.0
1300.0
1600.0
1900.0
2200.0
2500.0
Wavelength (nm)
Atmospheric Transmittance
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 O3 O2
H2O
CO2
CH4
RADIO
MICRO-
WAVE UV
GAMMA
VISIBLE
(VIS)
10-6 nm
10-5 nm
10-4 nm
10-3 nm
10-2 nm
10-1 nm
100 mm
100 km

8. Hyperspectral and Multispectral Band Passes

9. Atmospheric Scattering as a Function of Wavelength

10. Raw to Radiance (Data Calibration)
Raw DN includes: Surface Reflectance, Solar irradiance curve, Atmospheric effects (scattering, absorption), Variation in illumination due to topography, Instrument response
Raw to Radiance – remove instrument effects
Instrument calibration required to derive radiance coefficients
Raw DN * coefficients = Radiance 10

11. Atmospheric Effects and Surface Reflectance on Radiance
irradiance
wavelength
Radiance
irradiance
wavelength
Atmosphere
Radiance with atmospheric effects
irradiance
wavelength
Atmosphere
with atmospheric effects and ground reflectance

12. Radiance to Reflectance
L0(l)=Lsun(l) T(l) R(l) cos(q) + Lpath(l)
L0(l) = observed radiance at sensor
Lsun(l) = Solar irradiance above atmosphere
T(l) = total atmospheric transmittance
R(l) = surface reflectance
q = incidence angle
Lpath(l) = path scattered radiance
Conversion methods generally result in “apparent reflectance” because of topographic slope and aspect effects – variations in illumination are not corrected
Reflectance data scaled to get to integer data (typically x 10,000) 12

13. The Importance of Calibration and Atmospheric Correction
To compare multi-date images – some data sets even have different atmospheric properties across a scene
To compare data sets from different sensors
Needed for quantitative analysis, e.g., working with field data
convert to physical units – Radiance units: watts/sr*cm2*nm
When using band ratios such as vegetation indices
Reflectance data needed to compare data spectra with library reflectance spectra – helps in identifying materials based on their absorption features
Or to use spectral library to map materials, image must be in reflectance.

14. Advantages of Reflectance Data
Spectral features much more apparent in reflectance data than radiance
The shapes of spectra are principally influenced by the chemical and physical properties of surface materials
Reflectance data may be analyzed using spectroscopic methods that isolate absorption features and relate them to chemical bonds and physical properties of materials.

15. Survey! Do you work with raw, radiance, or reflectance data?
HSI or MSI data?

16. Multispectral Data Calibration

17. Multispectral Data Preprocessing
raw
radiance

18. Multispectral Data Preprocessing
reflectance
(with scattered light)
reflectance

19. Hyperspectral Data Preprocessing
Data from Santa Barbara, CA
With radiance data, the overall shape of this spectrum is strongly a function of the solar irradiance spectrum and absorption by atmospheric gases, especially water vapor
water vapor

20. Conversion to Reflectance Methods
Scene-derived corrections – in-scene statistics are used
Internal Average Relative Reflectance (IAR)
Flat Field
Log Residuals
Quick Atmospheric Correction (QuAC)
Ground-calibration methods
Empirical Line
Radiative transfer models
FLAASH

21. Internal Average Relative Reflectance (IAR)
The Internal Average Reflectance (IAR) approach uses the mean radiance of all the pixels in the image as a correction factor.
The individual radiance values in each pixel are divided by this mean radiance to estimate reflectance.
Removes common things
However, introduces artifacts

22. Flat Field Flat field - large, bright, homogenous target
The individual radiance values in each pixel are divided by the mean radiance of the flat field
Removes things in common

23. Log Residuals
Designed to remove solar irradiance curve, atmospheric transmittance, instrument gain, topographic effects, and albedo effects from radiance data
Defined as the input spectrum divided by the spectral geometric mean, then divided by the spatial geometric mean, creating a pseudo reflectance image
First calculate the spectral and spatial geometric means. Geometric means are calculated using logarithms of the data values and are used because the transmittance and other effects are multiplicative.
The spectral mean is the mean of all bands for each pixel and removes topographic effects
The spatial mean is the mean of all pixels for each band and accounts for the solar irradiance, atmospheric transmittance and instrument gain
Each image data value is then divided first by the spectral and then by the spatial mean

24. QuAC QUick Atmospheric Correction is fast
The approach is based on the finding that the spectral standard deviation (or endmember mean spectrum) of a collection of endmember spectra in a scene, is essentially spectrally flat
Works even when the sensor was not properly calibrated, or when the solar illumination intensity is unknown
Multi- or hyperspectral data can be raw, radiance, or apparent reflectance

25. QUAC continued
Does not work well in a scene that is not spectrally diverse. The scene should have several different materials.
The scene should have dark materials or shadows
QuAC is Batchable

26. Empirical Line Channel x dark target Image radiance bright target
Ground reflectance
Slope=gain
Intercept=offset
dark target
Image radiance
bright, homogenous
target
Reflectance=gain x radiance + offset
If only one spectrum is used, then the regression line will pass through the origin

27. Advanced Tools in ENVI for Conversion to Reflectance
Radiative Transfer – based
Models are developed that describe the radiative transfer of sunlight in its physical interaction with the gases and particles in the atmosphere, its interaction with the surface, and its transmission along a different path upward through the atmosphere to the sensor.
These models describe the solar irradiance curve, the absorption and scattering by atmospheric gases, and the reflectance from surface materials, all as a function of wavelength of electromagnetic radiation and the directional angles of the sun and sensor.
Errors arise from inadequate definition of the solar irradiance function, variations in the illumination, imperfect models that describe absorption by atmospheric gases, and any mis-calibration of the sensor.

28. FLAASH
FLAASH 4.1 – Fast Line of Site Atmospheric Analysis of Spectral Hypercubes
Supports many hyperspectral and multispectral Instruments: AVIRIS, HYDICE, HyMap, Probe-1, CASI, AISA, and HYPERION, Landsat, SPOT, IRS, IKONOS, QuickBird, ASTER, WorldView 2, etc.
Incorporates MODTRAN4 radiative transfer code
First, the optical characteristics of the atmosphere are estimated by using theoretical models.
Then, various quantities related to the atmospheric correction are computed by the radiative transfer algorithms given the atmospheric optical properties. Then, the data can be corrected by inversion procedures that derive the surface reflectance. Handles clouds, cirrus and opaque.
Gases corrected for: water vapor, ozone, oxygen, carbon monoxide, carbon dioxide, methane, and nitrous oxide
Water vapor the most variable and most important
Water modeled using three-band ratios around either the1135, 940 or 820 nm absorptions. Correction only possible where band positioning is appropriate.
Assumes that the surface is horizontal and has a Lambertian (diffuse) reflectance

30. FLAASH Parameters Sensor Type
Band passes and pixel size
Atmospheric Model - select appropriate model
Water Retrieval – to solve radiative transfer equations, water column needed
1135 nm is default – use unless there are materials in scene with absorptions at that wavelength then use 940 nm or 820 nm absorption
Aerosol Model – not critical if visibility over 40 km
Initial visibility – Clear: 40 to 100 km, Moderate Haze: 20—30 km, Thick Haze: 15 km or less
Spectral Polishing – well-behaved spectra used for calculation of gain factor. Used to remove artifacts due to:
Errors in radiative transfer models/calculations
Low signal in certain portions of the spectrum
Mis-calibration of sensor
Wavelength Recalibration – actual band positions determined from atmospheric features. Data sets can be re-run with new wavelength file.

31. EFFORT Polishing – stand alone routine
Empirical Flat Field Optimal Reflectance Transformation
Bootstrapped solution – “well behaved” flat reflectance sample spectra selected with replacement from all spectra
bootstrapping – sampling with replacement such that selected set can be treated as the entire population
Statistically mild gain (close to 1) and offset (close to 0) are calculated for each band – similar to empirical line correction
End result – artifacts removed and spectra more of a true indication of sensor SNR. Spectra can be more accurately compared to spectral library spectra

32. Raw Data versus Calibrated/Corrected Results
input data
color infrared
raw data
Maximum likelihood
classification
radiance data
Maximum likelihood
classification

33. Raw Data versus Calibrated/Corrected Results
input data
color infrared
raw data
Maximum likelihood
classification
radiance data
Maximum likelihood
classification

34. Raw Data versus Calibrated/Corrected Results
input data
color infrared
reflectance data
NDVI
Dark-corrected
reflectance data
NDVI

35. Hyperspectral Radiance versus Reflectance Data
input data

36. Hyperspectral SAM Results Comparison
input data
color infrared
radiance data
SAM result
reflectance data
SAM result
false positives
false positives

37. Some Applications that Rely on Atmospherically Corrected and Calibrated Data
Vegetation studies
NDVI, pigments, lignin and cellulose, species and community mapping,
Geological studies
Mineralogy, soils, rock types
Coastal and inland waters
Chlorophyll, suspended sediments, bottom composition
Snow and ice
Snow cover fraction, grain size
Environmental
Oil spills, other contaminants
Man-made infrastructure

38. For upcoming seminars and training, please visit:
For more information about ENVI’s capabilities or to request an evaluation:
For upcoming seminars and training, please visit: