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Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI …we will begin shortly. Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI. The phone lines will be muted for sound quality. - PowerPoint PPT Presentation

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

The information contained in this document pertains to software products and services that are subject to the controls of the Export Administration Regulations (EAR). The recipient is responsible for ensuring compliance to all applicable U.S. Export Control laws and regulations.

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

Page 2: Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI …we will begin shortly

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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

Page 3: Performing Quantitative Analysis with Remotely Sensed Imagery in ENVI …we will begin shortly

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Survey!

• Do you work on a Windows, Mac, or Unix machine?

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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

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Sun - Sensor Pathway

Solar Irradiance

Path Radiance(scattered light)

Radiance (reflected and emitted energy)

Sensor

absorbed

Absorbed by atmospheric gases

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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 (m)

Spec

tral

Irr

adia

nce

(W

/m2

m)

Blackbody at 5900K

Solar irradiance outside atmosphere

Solar irradiance at sea levelO3

H2O02, H2O

H2OH2O

H2OH2O

H2O, CO2

H2O, CO2H2O, CO2

(From Valley, 1965)

Solar Spectrum

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NEAR-INFRARED

(NIR)

SHORT WAVE INFRARED

(SWIR)

1 nm

10 n

m10

0 nm

1 m

10 m

1 mm

1 cm

10 cm 1 m 10 m

100 m 1 k

m10

km

400.0 700.0 1000.0 1300.0 1600.0 1900.0 2200.0 2500.0Wavelength (nm)

Atm

osp

her

ic T

ran

smit

tan

ce

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

O3O2

H2

OO2

H2O

H2O

H2O

H2O

H2O

O2 CO2

CO2

CH4

H2O

RADIOMICRO-WAVEINFRAREDUVGAMMA

VISIBLE(VIS)

VISIBLE

10-6 n

m10

-5 nm

10-4 n

m10

-3 nm

10-2 n

m10

-1 nm

100

m

100 k

m

The Electromagnetic Spectrum

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Hyperspectral and Multispectral Band Passes

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Atmospheric Scattering as a Function of Wavelength

Range of Atmospheric Scattering

Wavelength (m)

0.4 0.5 0.6 0.7 0.8 0.9 1.0

1

2

3

4

5

6

7

8

9

10

Rel

ativ

e Sc

atte

r

Range of Atmospheric Scattering

Wavelength (m)

0.4 0.5 0.6 0.7 0.8 0.9 1.0

1

2

3

4

5

6

7

8

9

10

Rel

ativ

e Sc

atte

r

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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

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Radiance

with atmospheric effects and ground reflectance

Atmosphere

Atmospheric Effects and

Surface Reflectance on

Radiance

Radiance with atmospheric effects

Atmosphere

wavelength

wavelength

wavelength

irra

dia

nce

irra

dia

nce

irra

dia

nce

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Radiance to Reflectance

L0()=Lsun() T() R() cos() + Lpath()

• L0() = observed radiance at sensor

• Lsun() = Solar irradiance above atmosphere

• T() = total atmospheric transmittance• R() = surface reflectance• = incidence angle

• Lpath() = 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)

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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.

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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.

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Survey!

• Do you work with raw, radiance, or reflectance data?

• HSI or MSI data?

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Multispectral Data Calibration

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Multispectral Data Preprocessing

rawradiance

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Multispectral Data Preprocessing

reflectance(with scattered light)

reflectance

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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

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Conversion to Reflectance Methods

1. Scene-derived corrections – in-scene statistics are used• Internal Average Relative Reflectance (IAR)• Flat Field• Log Residuals• Quick Atmospheric Correction (QuAC)

2. Ground-calibration methods• Empirical Line

3. Radiative transfer models• FLAASH

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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

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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

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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

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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

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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

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Empirical Line

bright, homogenous target

dark target

Imag

e ra

dian

ce

Channel x

dark target

bright target

Ground reflectance

Slope=gain

Intercept=offset

Reflectance=gain x radiance + offset

If only one spectrum is used, then the regression line will pass through the origin

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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.

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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

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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.

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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

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Raw Data versus Calibrated/Corrected Results

input datacolor infrared

raw dataMaximum likelihoodclassification

radiance dataMaximum likelihoodclassification

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Raw Data versus Calibrated/Corrected Results

input datacolor infrared

raw dataMaximum likelihoodclassification

radiance dataMaximum likelihoodclassification

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Raw Data versus Calibrated/Corrected Results

input datacolor infrared

reflectance dataNDVI

Dark-correctedreflectance dataNDVI

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Hyperspectral Radiance versus Reflectance Data

input data

radiance reflectance

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Hyperspectral SAM Results Comparison

input datacolor infrared

radiance dataSAM result

reflectance dataSAM result

false positives false positives

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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

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For more information about ENVI’s capabilities or to request an evaluation:

[email protected] 303.786.9900

www.ittvis.com

For upcoming seminars and training, please visit:

www.ittvis.com/EventsTraining