Emissivity spectra of rocks

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Thursday, 12 March
Lecture 20 review Labs questions Next Wed
Final 18 March 1030-1220
2

• physical basis of remote sensing
• spectra
• radiative transfer
• image processing
• radar/lidar
• thermal infrared
• applications

3
What is remote sensing? Measurement from a
distance Wide range of wavelengths
Hazardous locales
Images pixels DNs scanners, orbits image
geometry, parallax resolution color vs.
intensity and texture
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The spectrum and wavelength regions Units of
radiance, irradiance, spectral radiance Color
mixing, RGB false color images Color is due to
absorption e-kz (Beer Law) Hue, saturation,
intensity
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Radiative transfer Sunlight, atmospheric
absorption scattering Rayleigh, Mie,
Non-selective Reflection 1st surface
(Fresnels Law), volume Planck function l-5
(exp(c/lT)-1) e Atmospheric windows
DN g(ter tiItoacos(i)/p te rIs?/p
Ls?) o
r I cos(i)/p Lamberts law
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When do you need atmospheric compensation? dark
object subtraction Modtran model
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Interaction of Energy and Matter Rotational
absorption (gases) Electronic absorption
Charge-Transfer Absorptions Vibrational
absorption
Spectra of common Earth-surface materials
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Image processing algorithms radiometry geometry
Spectral analysis Statistical
analysis Modeling Algorithms Ratioing Spectra
l mixture analysis max number of endmembers
n1 shade NDVI
9
Classification spectral similarity supervised
vs. unsupervised maximum likelihood vs
parallelipiped themes land use validation con
fusion matrix
10
Confusion matrices Well-named. Also known as
contingency tables or error matrices Heres
how they work
All non diagonal elements are errors Row sums
give commission errors Column sums give
omission errors Overall accuracy is the
diagonal sum over the grand total This is the
assessment only for the training areas What do
you do for the rest of the data?
Training areas
A B C D E F
Row sums
A
0
5
0
0
0
485
480
B
0
52
0
20
0
72
0
C
0
0
Classified data
D
0
16
E
0
0
F
0
0
Grand sum
480
68
1992
p 586, LKC 6th
Col sums
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Crater counting relative dating on the moon and
Mars Forest remote sensing SMA in forest
studies Shade endmember vs. canopy vs.
topography What can Lidar do for forest
characterization?
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• Layover
• Shadows
• Polarization
• Sensitivity to
• - dielectric
• roughness

Corner reflectors Interferometry
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LiDAR
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Thermal
Plancks Law R e(l) c1p-1 l -5exp(c2
l-1T-1 )-1 -1
Emissivity
Blackbody radiation
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What compositions can be determined in the TIR?
Mostly vibrational resonance, not electronic
processes therefore, relatively large
molecules Silicate minerals (SiO4-4) quartz
(SiO2) Sulfates (SO4) sulfur dioxide
(SO2) Carbonates (CO3) carbon dioxide
(CO2) Ozone (O3) Water (H2O) Organic molecules
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Mauna Loa, Hawaii
MASTER VNIR daytime
ASTER TIR, daytime
MTI TIR, nighttime