1. Lecture 20 – review Thursday, 11 March 2010 Labs: questions
Next Wed – Final: 17 March 10:30-12:20

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

4. 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

5. Radiative transfer
Sunlight, atmospheric absorption & scattering
Rayleigh, Mie, Non-selective
Reflection – 1st surface (Fresnel’s Law), volume
Planck function: l-5 (exp(c/lT)-1) e
Atmospheric windows
DN = g·(te·r · ti·Itoa·cos(i)/p + te· r·Is↓/p + Ls↑) + o
r I cos(i)/p: Lambert’s law

6. When do you need atmospheric compensation?
dark object subtraction
Modtran model

7. Interaction of Energy and Matter
Rotational absorption (gases)
Electronic absorption
Charge-Transfer Absorptions
Vibrational absorption
Spectra of common Earth-surface materials

8. Image processing algorithms
radiometry
geometry
Spectral analysis
Statistical analysis
Modeling
Algorithms:
Ratioing
Spectral mixture analysis
max number of endmembers = n+1
shade
NDVI

9. Classification – spectral similarity
supervised vs. unsupervised
maximum likelihood vs parallelipiped
themes & land use
validation
confusion matrix

10. Well-named. Also known as contingency tables or error matrices
Confusion matrices
Well-named. Also known as contingency
tables or error matrices
Here’s how they work…
All non diagonal elements
are errors
Row sums give “commission”
errors
Column sums give “omission”
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
480 5
485 B 52 20 72 C
Classified data D 16 E F
Grand sum
480 68
1992
p 586, LKC 6th
Col sums

11. 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?

12. Layover
Shadows
Polarization
Sensitivity to
- dielectric
roughness
Corner reflectors
Interferometry

13. LiDAR

14. Thermal Planck’s Law: R = e(l) c1p-1 l -5[exp(c2 l-1T-1 )-1] -1
Emissivity
Blackbody radiation

15. 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

16. Mauna Loa, Hawaii MASTER VNIR daytime ASTER TIR, daytime MTI TIR,
nighttime