EOS 740 Hyperspectral Imaging Systems

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EOS 740 Hyperspectral Imaging Systems
February 18, 2005 Week 4
Ron Resmini v 703-735-3899 ronald.g.resmini_at_boein
g.com Office hours by appointment
Put EOS740 in the subject line of e-mails to
me...Thanks!
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Outline

• Scientific principles of HSI RS
• Remote sensing and sensor physics
• Physics of imaging spectroscopy
• Introduction to radiative transfer theory
• HSI analysis and exploitation with ENVI

Our first guest lecturer will be 4 March. Topic
HSI hardware. Pleasebe prepared to ask
questions!
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Primary References
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Scientific Principles of HSI RS

• HSI is based on the measurement of a physical
  quantityas a function of wavelength its
  spectroscopy, writ large
• HSI is based on discerning/measuring the
  interaction oflight (photons, waves) with matter
• The fundamental physical quantities of RS
• Sensors measure radiance as a function of
  wavelength
• Radiance (W/m2.sr.mm) (spectral)
• Spectral radiance is a flux of energy per solid
  angle
• Materials interact with electromagnetic radiation
  (EMR)
• Materials reflect, absorb, and/or transmit EMR

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• Other radiometric quantities, units, definitions
  of RS
• Irradiance (W/m2.mm) (spectral)
• Reflectance (r)
• Emissivity (e)
• Tables...there are lots of quantities
• Some important constants
• Speed of light, c, 2.9979 x 108 m/sec
• Plancks constant, h, 6.6256 x 10-34 joules.sec
• Boltzmanns constant, k, 1.38 x 10-23 joules/K
• c ln q (energy in a photon)hnhc/l (joules)
• n (cm-1) 1.0 x 104 / l (mm)
• The sun is the source of RS energy

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• Spectral ranges used in RS (see Richards and Jia,
  1999 pg. 3)
• Traditional HSI spectral ranges
• VNIR/SWIR (0.4 to 2.5 mm), MWIR (3 to 5 mm), LWIR
  (7 to 13 mm)
• Determined by h/w considerations and atmospheric
  windows
• Do not be so constrained when considering other
  apps. for HSI
• HSI is really a problem in inversion we sense
  the answerwe work backwards from there we
  sense boundary conditionsin one instant in time
• HSI, BTW, is remote material identification and
  characterization
• Key statement
• The spectrum is the fundamental datumin imaging
  spectrometry

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Remote Sensing and Sensor Physics

• Im not a h/w guybut
• Some practical information you should know to
  survive
• Dispersion the formation of spectra dispersion
  curve
• Prisms, gratings, interference (FTS)
• Imaging spectrometry the formation of images
• Push-broom whisk-broom other (e.g., FTS)
• What you need to know about your data a
  check-list
• Date, time, location, ground elevation, platform
  elevation,heading, GSD, of samples, of
  lines, of bands,band centers, band FWHM, band
  interleaving,byte order be able to calculate
  where the sun isi.e., all RS angles (geometry)

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• Radiometric and spectral calibration
• How theyre accomplished
• When ideally with every collection event
• Sensor drift
• On-going sensor characterization ask for it!
• Spatial sampling spatial resolution
• Spectral sampling SRF spectral resolution
• NESR, NEDr, NEDe, NEDT
• Issues smile, keystone, FPA misregistration,
  vibration,parallax, scattered light,
  self-emission, platformmotion/imaging
  distortions, etc
• Buyer beware - know the roles of the data
  providerand the data analyst

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Physics of Imaging Spectroscopy

• Origin of spectral features
• Electronic, vibrational, vibrational/rotational,
  etc
• Materials reflect, transmit, absorb, scatter
  lightbut first, why? how?
• Optical constants
• index of refraction, n
• imaginary part of refractive index, k
• ...related to absorption absorption coefficient
  isa 4pk/l
• aka complex refractive index, m nik
• This is really a convenience for solving PDEsof
  electromagnetic theory

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• Status check...where are we?
• Sensors measure radiance (spectral radiance)
• Materials interact with light
• m nik
• Whats reflectance?
• Tie it all together...
• The propagation of light
• Electromagnetic theory
• Solution of Maxwells Equations
• The Fresnel equations (pages from Hapke, 1993)
• BTW...Huygens Principle
• Snells Law/Law of Reflection
• Fermats Principle
• Polarization (not today...)
• What do you need to know?

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• For RS The types of scattering e.g.
• diffuse, specular idealized and
  reality(Schott, 1997 pg. 100) all describable
  withFresnel equations (and other...)
• Complicated, real surfaces and materials
• Minerals/rocks/mixtures (BTW...isotropic,uniaxial
  , biaxial)
• Vegetation
• Soils
• Water
• All real surfaces/materials!
• Is it all too complicated? No, spectral
  libraries...
• Mixed pixels (briefly more later in semester)
• The atmosphere(!)

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• So, can HSI (or any RS) help you? You must ask
• Is there a signature?
• How much is expected to be exposed/present?
• Other physical, chemical, radiative
  transferconsiderations
• E.g., littoral zone RS of coral under a turbid
  watercolumn that is under a turbid
  atmosphere...yikes!

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Introduction to Radiative Transfer (RT) Theory

• The RT equation
• Simplified expressions get you gt90 of whatyou
  need to know
• Radiometry and radiation propagation
  thisdiscussion is largely from Schott (1997),
  ch. 4
• Coordinates frames of reference principal
  plane, etc.
• Illumination angle, direction
• View angle, direction
• Phase angle
• Azimuth, relative/absolute

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The Radiative Transfer Equation
Eq. 7.21 on pg. 156 of Hapke (1993).
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Some Simplified RT Expressions

• RT can be (and in practice is) viewed as an
  accountingof terms based on radiance
  interactions in the RS scenario
• Bear in mind, however, that there is a link
  between theterms in the accounting and solutions
  to the RT equation
• The accountings can be as simple or as
  complicated asnecessary to address the RS
  question(s)/scenario(s)
• i.e., add terms, delete/ignore terms

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Solar/Reflective RS
For a horizontal surface
Now, add a thermal emission term
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The Big Equation
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The Big Equation (continued)
Theres an LI, too its the adjacency effectand
its sometimes included in the LC term.
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• VNIR/SWIR i.e., solar-reflective
• Thermal infrared i.e., emissive
• Defer both phenomena occurring together until
  later!
• Status check...where are we?
• What do we actually measure with an HSI sensor?
• We want to get to r or e
• Getting to reflectance (subject of future
  lecture)
• Types of reflectances (Hapke, 1993 pg. 183)

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Working with ENVI

• Spectral libraries in ENVI (continued)
• Multiple cubes linking data (a review)
• Exporting images for building products
• Band math, spectral math
• Information extraction (continued)
• Spectral matching review
• Whole pixel matching SAM (review)
• Band and spectral math
• Euclidean distance other algorithms

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What Were Going to Review

• Spectra as vectors points in hyperspace
• Angular separation of vectors (spectra)
• Spectral Angle Mapper (SAM)
• Invariant to albedo...wait a couple of slides
• Running SAM in ENVI
• Application strategies(i.e., in-scene
  spectra/library spectra)
• Mixed pixels...and SAM...

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The Geometry
Angular Distance Metric (Spectral Angle Mapper or
SAM)
Assume a two band spectral remote sensing system.
Each two point spectrum is a point in Band b
vs. Band a space.
A 2D scatterplot with 2 spectra
The angle, q, between the two lines connecting
each spectrum (point) to the origin is the
angular separation of the two spectra. Smaller
angular separations in- dicate more similar
spectra.
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The Math

• Chang (2003), ch. 2, pp. 20-21 (see .pdf
  file) and...
• Assume two 5-band spectra as shown

BTW...read Sec. 2.2 to Sec. 2.2.2 on pp. 20-21
fair game material for the mid-term.
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• Let the 5 bands have band names a, b, c, d, and e

• The output units are radians
• ENVI does all this for you

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• Invariant to albedo...why

A 2D scatterplot with 2 spectra
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• Application strategies
• A few comments on SAM andmixed pixels
  (introduction)
• Ch. 2 in Chang (2003) can/shouldENVI do any of
  these?

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• Band math
• Spectral math
• CBD, ED from Chang (2003)

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Assignment 2 Due the Week of 25 Feb., 2005

• Open the urban HSI reflectance data cube.
• Make sure bad bands have been removed.
• Map the carbonates in the scene i.e., use SAM
  anda library calcite spectrum. Watch your
  scaling factors!
• Map the carbonates in the scene again use ED
  andthe same library calcite spectrum.
• Repeat steps 3 and 4 but use only the bands
  covering the1.9920 mm (band 155) to 2.3990 mm
  (band 198)spectral range.
• Create a report presenting the results you
  should have,at a minimum, a color composite of
  the original data andfour (4) graphics showing
  the mapping results. A word slide(bullet list)
  describing your procedure is also required.
• Compare the four mapping results a brief write
  up of 100words should suffice.

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Project Challenges Ive added some...

• N-P Theory sensitivity to spatial/spectral
  subsets
• When is spectral mixing linear v. non-linear?
  I.e., is this evidentfrom the spectra?
• Measure the volume of hyperspace actually
  occupied by real HSI data
• Spectral angle between spectra and filter
  vectors is the separabilitygreater than angles
  derived from a confusion matrix analysis of
  aspectral library? Use, also, a measure of SCR
• Make Mine Virginia Wine. Characterize VA
  vineyard soils with HyperionHSI and/or field
  spectrometry characterize grape vines
  etc...Does HSI have a role in the VA wine
  business?
• Test various algorithms with target-implanted HSI
  data sets
• Compare recently published TES routines
• Evaluate noise removal/compensation algorithms
• Spectral indicators for urban lead poisoning and
  medical geology

How are your projects going?
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Project Challenges (continued)

• Invert the SAIL canopy RT model with noise...
• Implement, compare, test algorithms from the
  textbook
• Assess impact of spectral MTF on subpixel
  unmixing
• GSD and the geometry of hyperspace...
• Derivative spectroscopy and vegetation RS e.g.,
  REDE
• Optical bathymetry (e.g., swimming pools)
• Continuation of projects from last semester

BTW...Contributions to scientific knowledgevice
generic techniques studies are also strongly
encouraged.