Transcript Emissivity spectra of rocks
Lecture 20 – review
Labs: questions Next Wed – Final:
18 March 10:30-12:20 Thursday, 12 March
-
physical basis of remote sensing
-
spectra
-
radiative transfer
-
image processing
-
radar/lidar
-
thermal infrared
-
applications
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
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
Radiative transfer Sunlight, atmospheric absorption & scattering
Rayleigh, Mie, Non-selective
Reflection – 1 st surface (Fresnel’s Law), volume Planck function: l -5 (exp(c/ l T)-1) e Atmospheric windows DN = g·( t e ·r · t i ·I toa ·cos(i)/ p + t e · r·I s↓ / p + L s↑ ) + o r I cos(i)/ p : Lambert’s law
When do you need atmospheric compensation?
dark object subtraction Modtran model
Interaction of Energy and Matter Rotational absorption (gases) Electronic absorption
Charge-Transfer Absorptions
Vibrational absorption
Spectra of common Earth-surface materials
Image processing algorithms radiometry geometry Spectral analysis Statistical analysis Modeling Algorithms: Ratioing Spectral mixture analysis
max number of endmembers = n+1 shade
NDVI
Classification – spectral similarity supervised vs. unsupervised maximum likelihood vs parallelipiped themes & land use validation confusion matrix
Confusion matrices Well-named. Also known as contingency tables or error matrices
Here’s how they work…
A B C D E F Training areas A B C D E F 480 0 5 0 0 0 0 0 0 0 52 0 0 0 16 480 Col sums 68 0 20 0 0 0 Row sums 485 72 1992 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?
Grand sum
p 586, LKC 6 th
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?
Layover Shadows Polarization Sensitivity to - dielectric - roughness
h
l 8 cos
i
Corner reflectors Interferometry
LiDAR
Thermal
Planck’s Law: R = e ( l ) c 1 p -1 l -5 [exp(c 2 l -1 T -1 )-1] -1 Emissivity Blackbody radiation
What compositions can be determined in the TIR?
Mostly vibrational resonance, not electronic processes therefore, relatively large molecules Silicate minerals (SiO 4 -4 ); quartz (SiO 2 ) Sulfates (SO 4 = ); sulfur dioxide (SO 2 ) Carbonates (CO 3 = ); carbon dioxide (CO 2 ) Ozone (O 3 ) Water (H 2 O) Organic molecules