1 / 40

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.

talulah-taylor
Télécharger la présentation

Chapter 5

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related