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Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20

Thursday, 12 March. Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20. physical basis of remote sensing spectra radiative transfer image processing radar/lidar thermal infrared applications. What is remote sensing? Measurement from a distance

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Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20

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  1. Thursday, 12 March Lecture 20 – review Labs: questions Next Wed – Final: 18 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. 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” 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

  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(c2l-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

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