remote sensing and image processing: 3

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Remote Sensing and Image Processing: 3

Dr. Mathias (Mat) Disney

UCL Geography

Office: 301, 3rd Floor, Chandler House

Tel: 7670 4290

Email: mdisney@geog.ucl.ac.uk

www.geog.ucl.ac.uk/~mdisney

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Back to the process....

• What sort of parameters are of interest?

• Variables describing Earth system....

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EO and the Earth

“System”

From Ruddiman, W. F., 2001. Earth's Climate: past and future.

External forcing

Hydrosphere

Atmosphere

Geosphere

Cryosphere

Biosphere

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Example biophysical variables

After Jensen, p. 9

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Example biophysical variables

After Jensen, p. 9

Good discussion of spectral information extraction:

http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf

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Remote Sensing Examples

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Information extraction process

After Jensen, p. 22

Image interpretation

•Tone, colour, stereo parallax

•Size, shape, texture, pattern, fractal dimension

•Height/shadow

•Site, association

Primary elements

Spatial arrangements

Secondary elements

Context

Analogue image

processing

•Multi:•spectral, spatial, temporal, angular, scale, disciplinary

•Visualisation

•Ancillary info.: field and lab measurements, literature etc.

Presentation of information

•Multi:•spectral, spatial, temporal, angular, scale, disciplinary

•Statistical/rule-based patterns

•Hyperspectral

•Modelling and simulation

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Example: Vegetation canopy modelling•Develop detailed 3D models

•Simulate canopy scattering behaviour

•Compare with observations

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• Core principles of electromagnetic radiation (EMR)– solar radiation– blackbody concept and radiation laws

• EMR and remote sensing– wave and particle models of radiation– regions of EM spectrum– interaction with atmosphere– interaction with surface

• Measurement of radiation

Electromagnetic (EM) Spectrum

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• This is what we measure in remote sensing

• Terms, units, definitions

• Provide basis for understanding type of information that can be retrieved

• Why we choose given regions of the EM spectrum in which to make measurements

EM spectrum: so what?

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Remote sensing process: recap

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• Note various paths– Source to sensor direct?

– Source to surface to sensor

– Sensor can also be source• RADAR, LiDAR, SONAR

• i.e. “active” remote sensing

• Reflected and emitted components– What do these mean?

• Several components of final signal captured at sensor

Remote sensing process: recap

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• Conduction– transfer of molecular kinetic (motion) energy due to contact

– heat energy moves from T1 to T2 where T1 > T2

• Convection– movement of hot material from one place to another

– e.g. Hot air rises

• Radiation– results whenever an electrical charge is accelerated

– propagates via EM waves, through vacuum & over long distances hence of interest for remote sensing

Energy transport

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EM Spectrum

•EM Spectrum

•Continuous range of EM radiation

•From very short wavelengths (<300x10-9m)

•high energy

•To very long wavelengths (cm, m, km)

•low energy

•Energy is related to wavelength (and hence frequency)

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• Energy radiated from sun (or active sensor)• Energy 1/wavelength (1/)

– shorter (higher f) == higher energy– longer (lower f) == lower energyfrom http://rst.gsfc.nasa.gov/Intro/Part2_4.html

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Units•EM wavelength is m, but various prefixes

•cm (10-2m)

•mm (10-3m)

•micron or micrometer, m (10-6m)

•Angstrom, Å (10-8m, used by astronomers mainly)

•nanometer, nm (10-9)

•f is waves/second or Hertz (Hz)

•NB can also use wavenumber, k = 1/ i.e. m-1

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EM Spectrum

•We will see how energy is related to frequency, f (and hence inversely proportional to wavelength, )

•When radiation passes from one medium to another, speed of light (c) and change, hence f stays the same

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Electromagnetic spectrum: visible

• Visible part - very small part– from visible blue (shorter

)

– to visible red (longer )

– ~0.4 to ~0.7mViolet: 0.4 - 0.446 m

Blue: 0.446 - 0.500 m

Green: 0.500 - 0.578 m

Yellow: 0.578 - 0.592 m

Orange: 0.592 - 0.620 m

Red: 0.620 - 0.7 m

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Electromagnetic spectrum: IR

• Longer wavelengths (sub-mm)

• Lower energy than visible

• Arbitrary cutoff

• IR regions covers – reflective (shortwave IR,

SWIR)

– and emissive (longwave or thermal IR, TIR)

– region just longer than visible known as near-IR, NIR.

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Electromagnetic spectrum: microwave

• Longer wavelength again

– RADAR

– mm to cm

– various bands used by RADAR instruments

– long so low energy, hence need to use own energy source (active wave)

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Electromagnetic spectrum

• Interaction with the atmosphere– transmission NOT even across the spectrum– need to choose bands carefully to coincide with regions where

transmission high (atmospheric windows – see later)

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“Blackbody” concept•All objects above absolute zero (0 K or -273° C) radiate EM energy (due to vibration of atoms)

•We can use concept of a perfect blackbody

•Absorbs and re-radiates all radiation incident upon it at maximum possible rate per unit area (Wm-2), at each wavelength, , for a given temperature T (in K)

•No real object is blackbody but it is v. useful assumption

•Energy from a blackbody?

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Stefan-Boltzmann Law

•Total emitted radiation from a blackbody, M, in Wm-2, described by Stefan-Boltzmann Law

4TM

•Where T is temperature of the object in K; and = is Stefan-Boltzmann constant = 5.6697x10-8 Wm-2K-4

•So energy T4 and as T so does M

•Tsun 6000K M,sun 73.5 MWm-2

•TEarth 300K M , Earth 460 Wm-2

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Stefan-Boltzmann Law

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Stefan-Boltzmann Law

•Note that peak of sun’s energy around 0.5 m

•negligible after 4-6m

•Peak of Earth’s radiant energy around 10 m

•negligible before ~ 4m

•Total energy in each case is area under curve

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Peak of emitted radiation: Wien’s Law•Wien deduced from thermodynamic principles that energy per unit wavelength E() is function of T and

•At what m is maximum radiant energy emitted?

•Comparing blackbodies at different T, note mT is constant, k = 2897mK i.e. m = k/T

m, sun = 0.48m

m, Earth = 9.66m

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)((

Tf

E

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Wien’s Law

•AKA Wien’s Displacement Law

•Increase (displacement) in m as T reduces

•Straight line in log-log space

Increasing

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Planck’s Law of blackbody radiation

•Planck was able to explain energy spectrum of blackbody

•Based on quantum theory rather than classical mechanics

•dE()/d gives constant of Wien’s Law

E() over all results in Stefan-Boltzmann relation

•Blackbody energy function of , and T

1

125

2

kT

hc

e

hcE

http://www.tmeg.com/esp/e_orbit/orbit.htm

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Planck’s Law•Explains/predicts shape of blackbody curve

•Use to predict how much energy lies between given •Crucial for remote sensing

http://hyperphysics.phy-astr.gsu.edu/hbase/bbrc.html#c1

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Consequences of Planck’s Law

•Chlorophyll a,b absorption spectra

•Photosynthetic pigments

•Driver of (nearly) all life on Earth!

•Source of all fossil fuel

•Allows us to explain radiant energy distribution of any object (e.g. sun)

•Predict at what peak energy is emitted and so choose our spectral bands accordingly

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Recap• Physical properties we might measure

– E.g. reflectance, temperature, height etc.

• EM radiation is what we measure in RS• Blackbody concept used to explain energy

distribution of sun / Earth– Stefan-Boltzmann law explains total energy– Wien’s law explains shift of max with decreasing T– Planck’s Law explains shape of BB energy distribution– BUT remember, no object is really a blackbody – only an

approximation

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MODIS: building global picture

From http://visibleearth.nasa.gov/Sensors/Terra/

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IKONOS & QuickBird: very local view!

•QuickBird: 16.5km swath at nadir, 61cm! panchromatic, 2.44m multispectral

•http://www.digitalglobe.com

•IKONOS: 11km swath at nadir, 1m panchromatic, 4m multispectral

•http://www.spaceimaging.com/

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Ikonos: high res. commercial

http://www.spaceimaging.com/gallery/spacepics/khaolak_side_by_side.jpg

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Ikonos: high res.

commercialhttp://www.euspaceimaging.com/sime.asp?page=Gallery