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Chapter 5

Chapter 5. Multispectral, thermal and hyperspectral scanning Introduction to Remote Sensing Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng-Kung University Last updated: 7 May 2003. 5.1 Introduction.

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Chapter 5

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  1. Chapter 5 Multispectral, thermal and hyperspectral scanning Introduction to Remote Sensing Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng-Kung University Last updated: 7May 2003

  2. 5.1 Introduction • MSS (multispectral scanning) vs. MCS (Multiband camera system) • Fig 2.42, 2.43 (MCS) • Advantages • Spectral bands: 0.3~1.4 mm with narrow band width • Collection: same optical system for all bands. • Calibration: electronically rather than photochemically. • Storage and transmission

  3. 5.2 Across-track multispectral scanning • Across-track (whiskbroom) scanner: • Fig 5.1: operation of a five-channels sensor •  flight line • Using a rotating or oscillating mirror (Fig 5.1b) • Contiguous strips  2D image • Dichroic grating  two forms of energy • Thermal • Nonthermal  prism  UV, Vis and near-IR • Detectors  spectral sensitivity • Signal  amplified  recorded  A-to-D  View-in-flight  storage  interpretation

  4. 5.2 Across-track multispectral scanning (cont.) • An example of multispectral sensor • IFOV (instantaneous field of view) • the cone angle with which incident energy is focused on the detector (see b in Fig 5.1a) • See “pure” and “mixed” pixels simultaneously. • Resolution • Spatial resolution: D=H΄b (Fig 5.2) • Ground resolution element (resolution cell) • Towards edge  resolution cell  (image distortion). • For typical airborne MSS system: b = 0.5 ~ 5 (mrad)

  5. 5.2 Across-track multispectral scanning (cont.) • Trade-off between spatial resolution and radiometric resolution. • IFOV  •  signal-to-noise ratio  radiometric resolution  •  spatial resolution  • Fig 5.3: eleven-band digital across-track MSS system. • 7 of them  Landsat Thematic Mapper.

  6. 5.2 Across-track multispectral scanning (cont.) • Fig 5.4: eleven-band MSS images • (A) street  (B) grass: • clear contrast in all bands • Tonal reversal: from band 8~10 (near-IR) • (C) water  (B) grass: • difficult to differentiate in the Vis-band, but clean contrast in near-IR bands. • (D) cloud shadow: • channel 1:somewhat illuminated by Rayleigh scatter • near-IR  low Rayleigh scatter  darkest • thermal  no signal (moving clouds)

  7. 5.2 Across-track multispectral scanning (cont.) • Fig 5.5: eight-band MSS image • Pavement  grass: • clear in Vis-band(1~3) • tones reverse in near-IR(4, 5) and mid-IR (6, 7) • River plume: clear in thermal band (8) • Dock: clear in band (6~8)

  8. 5.3 Along-track multispectral scanning • Along-track (pushbroom) scanner • Fig 5.6 • A linear array of CCDs (charge-coupled device) • No scanning mirror • Size of detectors  ground resolution • The smaller the better • Each band  one array

  9. 5.3 Along-track multispectral scanning (cont.) • Along-track (pushbroom) scanner (cont.) • Pros: • Longer dwell time (residence time) • Stronger and greater range of signal • Better spatial and radiometric resolution • Fixed detector  geometric integrity  • Size & weight & power  • No moving part  reliability & life expectancy  • Cons: • Calibration • Limited range of spectral sensitivity (<near –IR) • Underdeveloping

  10. 5.3 Along-track multispectral scanning (cont.) • Fig 5.7 MEIS II • the first airborne pushbroom scanner • 1728-element linear arrays • 8 spectral bands (0.39 mm~1.1 mm) • IFOV 0.7 m rad TFOV 400 • 8-bit (256 DN)

  11. 5.3 Along-track multispectral scanning (cont.) • Fig 5.8: MEIS II in stereomode. • External mirror  forward-looking & aft-looking • MOMS ( the 1st space borne pushbroom scanner)

  12. 5.4 Across-track thermal scanning • Thermal portion of the spectrum • Atmospheric window  two ranges (3~5mm, 8~14 mm) • Rapid response (<1-msec) • Photon  electrical charge • Dewar  cool detector • 3 photon detectors in common use today • Table 5.1: spectral sensitivity range

  13. 5.4 Across-track thermal scanning (cont.) • Fig 5.9 • Across-track thermal scanner schematic • Recording procedure: • Incoming energy • Additional optics • Focusing  detector • Detector (encased by a dewar)  signal • Amplify • Display &record, amplified signal  modulate tube • Scan • Record • Platen • Film advanced rate = fn(V/H΄) • Fig 5.10: a typical thermal scanner system

  14. 5.5 Thermal radiation principles • Radiant versus kinetic temperature • Kinetic temperature: contact  internal manifestation  average translational energy of the molecules • Radiant temperature: emitted energy  external manifestation  object’s energy state • Blackbody radiation • Fig 5.11: Spectral distribution of energy radiated from blackbodies of various temperatures • Wien’s displacement law • Stefan-Boltzmann law

  15. 5.5 Thermal radiation principles (cont.) • Radiation from real materials • No perfect blackbody • Emissivity: e(T)  Mreal(T) / Mblack body(T) • 0 < e < 1 • e =fn(l, qviewing, T) • Graybody e  fn(l) • Selective radiator e = fn(l) • Fig 5.12 • Fig 5.13: (6~14mm) • Water ~ blackbody (e = 0.98~0.99) • Quartz ~ selective radiator

  16. 5.5 Thermal radiation principles (cont.) • Radiation from real materials (cont.) • Importance of M(l = 8~14 mm)  thermal sensing • Atmospheric window • M(T=300K) = 9.7 mm • For broadband sensor  treated as graybody • Table 5.2: typical values of emissivity over 8~14 mm • Atmospheric effects • Atmospheric windows: Fig 5.14 • Thermal range : (3~5 mm, 8~14 mm) • Atmospheric absorption & scattering  colder objects. Atmospheric emission  warmer object • Biased sensor output • Compensation  later this chapter

  17. 5.5 Thermal radiation principles (cont.) • Interaction of thermal radiation with terrain elements. • EI=EA+ER+ET conservation of energy. 1 = a +r + t  normalize 1 = e +r + t  Kirchhoff radiation law (good absorbersgood emitters) 1= e +r assumption of opaque to thermal radiation. • For real body: • M= esT4 • Trad=e1/4Tkin • Table 5.3: typical values • Always underestimate T if neglect e effect • Surface T (<50mm)  internal bulk T

  18. 5.6 Interpreting thermal scanner imagery • Successful interpretations • Rock type, structure • Locating geologic faults • Mapping soil type & moisture • Locating irrigation canal leaks. • Volcanoes. • Evapo-transpiration from vegetation. • Locating cold water springs. • Locating hot water springs & geysers. • Thermal plumes in lakes and rivers. • Natural circulation patterns in water bodies. • Forest fires. • Locating subsurface fires in landfills or coal refuse piles

  19. 5.6 Interpreting thermal scanner imagery (cont.) • Most application qualitative Some application  quantitative • Considerations of the time for acquiring thermal data • Fig 5.15: generalized diurnal radiant T variations. • Water  range of T is small Water  Tmax(water)  Tmax(rock)+1~2 • Crossovers • Thermal conductivity • Thermal capacity • Thermal inertia ( conductivity) • Predawn imagery  preferred but difficult

  20. 5.6 Interpreting thermal scanner imagery (cont.) • Fig 5.16: day time and night time imagery • Water • Land • Trees • Paved area • Fig 5.17: day time thermal imagery • Beach ridge(B) • Lakebed soil(A) • Trees( C ) • Bare soil (D) • Mowed grass (E)

  21. 5.6 Interpreting thermal scanner imagery (cont.) • Fig 5.18: night time thermal imagery • Cows • Deers • Metal roof • Fig 5.19: high resolution daytime thermal image • Helicopter shadows • Fig 5.20: Daytime thermal imagery • Hot water • Circulations

  22. 5.6 Interpreting thermal scanner imagery (cont.) • Fig 5.21 nighttime thermal image depicting building heat loss. • Fig 5.22 Thermovision camera and display unit • Fig 5.23 Thermal images showing streamline heat loss. • NASA’s Thermal infrared Multispectral Scanner (TIMS). • Plate 10(a). TIMS image • Plate 10(b). A generalized lithologic map

  23. 5.7 Geometric characteristics of across-track scanner imagery • Along-track scanner  no mirror, fixed geometric relationship • Across-track scanner  systematic and random geometric variations • Spatial resolution and ground coverage • Table 5.4 • W= 2H΄tan

  24. 5.7 Geometric characteristics of across-track scanner imagery (cont.) • Tangential scale distortion (TSD) • Fig 5.24: Source of tangential scale distortion • dq /dt=constdoesn't mean dx/dt=const • Fig 5.25: the effect of tangential distortion • Constant longitudinal scale & varying lateral scale • S-shaped sigmoid curvature • Fig5.26: Comparison of aerial photo & across-track thermal scanner image • Fig 5.27: tangential scale distortion • compression near the top-bottom edge

  25. 5.7 Geometric characteristics of across-track scanner imagery (cont.) • Tangential scale distortion (cont.) • Fig 5.28 correction of TSD • qp= ypqmax/ymax • Yp= H΄tanqp • Electronically or digitally correct TSD  rectilinearized images

  26. 5.7 Geometric characteristics of across-track scanner imagery (cont.) • Resolution cell size (RCS) variations • Fig 5.29 • // fight direction  H΄b Hq΄b =H΄secqb •  flight direction  H΄b Hq ΄secq=Hq΄bsecq=H΄sec2qb • Response time: time that a scanner takes to respond electronically to a change in ground reflected or emitted energy • Variations of RCS can be 3~4 times along a scan line • Limitation of RCS to the image analysis • Object>pixel(RCS) representative  analysis • Compensation of irradiance fall-off due to RCS

  27. 5.7 Geometric characteristics of across-track scanner imagery (cont.) • 1-D relief displacement (RD) • Fig 5.30: RD in a vertical aerial photo vs an across-track scanner image • Dynamic continuous process sensitive to the aircraft attitude deviations (Fig 5.34) • Fig 5.31: across-truck thermal scanner image illustrating 1-D RD.

  28. 5.7 Geometric characteristics of across-track scanner imagery (cont.) • 1-D relief displacement (RD) • Fig 5.32: 1-D RD and TSD • Fig 5.33: Effect of nonsynchronized image recording rate and V/H΄ ratio • Fig 5.34: distortions induced by aircraft attitude deviations • Fig 5.35: effect of roll compensation

  29. 5.8 Radiometric calibration of thermal scanners • Thermal scanner image  lack of geometric integrity • Take photo simultaneously • Night time use daytime photo (even old photos will do)

  30. 5.8 Radiometric calibration of thermal scanners (cont.) • Two methods of radiometric calibration • Internal blackbody source referencing • Two controlled sources: cold & hot • View the sources during every scan line • Fig 5.36: configuration • A typical mission: height 600m0.30C but atmospheric condition can be 20C • Air-to-ground correlation: • Account for atmospheric effects • Theoretical approach • Empirical approach  the general approach • Fig 5.37 a sample of calibration curve • Fig 5.38 a thermal radiometer used for air-to-ground correlation measurements

  31. 5.9 Temperature mapping with thermal scanner data • Many applications Map • Two procedures • Image-based • Crude form: qualitative depiction low geometric and radiometric integrity • Suffice for many application • Tonal levels  densitometric analysis  rectilinerarized imagery. • Numerically based • N=A+BM=A+BeT4 • N: DN • A, B : to be determined • e: emissivity • T: kinetic temperature • Two unknown (A, B)  two equations (data)

  32. 5.10 FLIR systems • Forward-looking infrared (FLIR) system oblique views of the terrain ahead of an aircraft • Fig 5.39 • Point forward sweep across the scene of interest • Military applications

  33. 5.11 Imaging spectrometry • Hyperspectral  multispectral • Fig 5.40 • Fig 5.41: Selected laboratory spectra of minerals • Hyperspectral data  detailed identification of materials and quantification of their abundance • Airborne Imaging Spectrometer (AIS) • 128 channel from 1.2~2.4 mm with 9.3 nm band width • 1.9 mrad, 4200m high, resolution 8x8m, swath 32 pixel

  34. 5.11 Imaging spectrometry (cont.) • Airborne Imaging Spectrometer (cont.) • Fig 5.42: AIS images • Condominium complex • a: school • b: field • Loss of detail in the atmospheric water absorption band (1.4mm) • Watered courtyard lawn a (compared to unwatered field b) • Water vapor absorption bands 0.94, 1.14, 1.4, 1.88 mm

  35. 5.11 Imaging spectrometry (cont.) • Fig 5.43: selected laboratory spectra of green leaves • Main components: chlorophyll and water • Shape + peak+valley  species • Fig 5.44: same as Fig 5.43 but single species • Main components: lignin & holocellulose • Difference  green or dry  holocellulose • Assessing biomass • Vegetation stress • Carbon cycle • Discriminating plant communities • Phenological conditions

  36. 5.11 Imaging spectrometry (cont.) • Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) • 224 channels, 0.4~2.45 mm with bandwidth 9.6nm • Altitude: 20km, resolution 20m, swath: 10km • Plate 11: hyperspectral image cube • A common way to display hyperspectral data • Color composite: 0.557(b), 0.815(g), 2.209(r) mm • Fig 5.45 • Three stages: green leaf initiation  development  change in plant pigment proportions  flowering  domancy  leaf loss  senescence • Applications • AVIRIS data  spectra matching  library  mineral type • An export system-based analysis approach

  37. 5.11 Imaging spectrometry (cont.) • Compact Airborne Spectrographic Imager (CASI) • Commercial, programmable • 288 channels from 0.4~0.9mm with bandwidth 1.8 nm • IFOV= 1.2 mrad

  38. 5.11 Imaging spectrometry (cont.) • Geophysical and Environmental Research Imaging Airborne Spectrometer (GERAIS) • 63 channels, 0.4~2.48 mm • Fig 5.46: GERAIS Image for identifying minerals • Label A in channel 41  dark spot  alunite • Alunite  dark from 41~44  absorption light from 49~52  reflection • Red edge • The slope of a reflectance spectrum from 0.68~0.76 mm • Shift  change in the chemical and morphological status  heavy metals in the soil

  39. 5.11 Imaging spectrometry (cont.) • DIAS-7915 • ASDIS-204

  40. 5.12 Conclusion

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