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Transcript of 9 Remote Sensing - TU Braunschweig Physical Basics 9.2 Recording Techniques ... •Theodolite:...
9.1 Physical Basics
9.2 Recording Techniques
9.3 Image Processing
9.4 Thematic Classification
9.5 Summary
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 704
9 Remote Sensing
http://saturn.unibe.ch/.../Fotogrammetrie-Bildflug.pdf
• A geographic information system (GIS) is a computer hardware and software system designed to
– Collect
– Manage
– Analyze
– Display
geographically referenced data (geospatial; spatial)
• It is a specialized information system consisting of a (spatial) database and a (special) database system
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 705
9 Remote Sensing
• Recording on site
– Terrestrial survey techniques
• Global navigation satellite systems
(e.g. GPS)
• Very long baseline interferometer
(VLBI)
• Theodolite: measuring both
horizontal and vertical angles
optically
• Total station: electronic theodolite
(transit) integrated with an
electronic distance meter
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 706
9 Remote Sensing
http://de.wikipedia.org/
www1.tu-darmstadt.de
www.photolib.noaa.gov
http://tu-dresden.de/
– Hydrographic survey
• Sounding
– Thematic survey
• Map digitization
• Survey by different sources
– Statistics
– Ministerial data
– Technical literature
• Aerial survey and survey by remote sensing
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 707
9 Remote Sensing
http://tu-dresden.de/die_tu_dresden/…/papers/fuhrland.pdf
• Remote sensing is the acquisition of information of an object or phenomenon, by the use of device(s) that are not in physical or intimate contact with the object → indirect observation technique– That uses the electromagnetic
radiation which is emitted by the observed object
– That carries receiving devices on aircraft or spacecraft
– That serves for the observation of the surface of the earth including all objects thereon, the oceans or the atmosphere
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 708
9 Remote Sensing
http://www.etsu.edu/cas/geosciences/
• Photogrammetry
– Greek: photo - grammetry ≈ image-measurement
– Acquisition and analysis of images to determine the
properties, form and position of arbitrary objects
– Remote sensing is the acquisition of
physical properties of objects whereas
photogrammetry is the reconstruction
of their geometric form
based on this data
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 709
9 Remote Sensing
http://www.gisdevelopment.net/…/mm063d_155.htm www.maps.google.de
• System Characteristics
– Recording techniques
• Radiometric resolution
• Geometric resolution
– Platform
• Kind of platform
• Altitude
• Orbit
• Period
– Mission
• Temporal coverage
• Spatial coverage
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 710
9 Remote Sensing
www.atmos.albany.edu/deas/atmclasses/atm335/history.pdf
www.irs.uni-stuttgart.de
• Electromagnetic waves as information carrier
– Straight propagation with the speed of light
– Speed of light = wavelength x frequency
– Longer wavelength, lesser energy → more difficult to
sense
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 711
9.1 Physical Basics
electrical field
distance
magnetic field M
E
cspeed of light
ν: frequency
λ: wavelength
number of cycles that passesa certain point per second
http://www.fe-lexikon.info/images/ElektromagnetischeWelle.jpg
• Electromagnetic spectrum
– The electromagnetic spectrum is the range of all
possible frequencies of electromagnetic radiation
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 712
9.1 Physical Basics
http://de.wikipedia.org/wiki/Bild:Elektro-magnetisches_Spektrum.JPG
• Behavior of electromagnetic waves at interfaces
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 713
9.1 Physical Basics
Reflection
EmissionAbsorption
Transmission
Scattering
Transmission + Reflection + Absorption = 1
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 714
9.1 Physical Basics
[Al07]
solar radiation
sensor
received signal
scattered lightatmospheric absorption
and scatteringsky radiation
reflection at the surface scattering at the surface
absorption and reflection in the water (suspended
particles)reflection at the ground
water depth
– The albedo (lat. albedo = „whiteness“), reflectivity
• The extent to which an object diffusely reflects light from
the sun
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 715
9.1 Physical Basics
– Albedo depends on wavelength
• There is a strong difference between visual and infrared
albedos of natural materials
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 716
9.1 Physical Basics
[Al07]
• The sun is the most important source of
electromagnetic radiation
• With the exception of objects at absolute zero, all
objects emit electromagnetic radiation
– The higher the temperature,
the shorter the wavelength
of maximum emission
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 717
9.1 Physical Basics
www.eduspace.esa.int/eduspac e/.../images/03.jpg
• Blackbody
– Hypothetical source of energy that behaves in an
idealized manner
– It absorbs all incident radiation, none is reflected
– It emits energy with perfect efficiency
– Its effectiveness as a radiator of energy varies only as
temperature varies
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 718
9.1 Physical Basics
http://mynasadata.larc.nasa.gov/images/BB_illustration2.jpg
• Emissivity
– The ratio between the emitance of a given object and that of blackbody at the same temperature
– Useful measure of the effectiveness of objects as radiators
– Kirchhoff„s law: At thermal equilibrium, the emissivity of a body (or surface) equals its absorptivity
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 719
9.1 Physical Basics
surface emissivity (8-14 μm)
blackbody 1
water, dependingon pollution
0,973-0,979
water with oil film
0,96-0,979
snow 0,99
grass, dense, short
0,92-0,97
Sands,depending on water moisture
0,88-0,985
• Atmospheric window
– Ultraviolet 0.01 - 0.4 μm
• Reflected solar radiation
• Because of atmospheric absorption it can only be used on
aircrafts flying at low altitude
• Main application: oil contamination detection in water (it is
possible to identify the ship which has lost the oil!)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 720
9.1 Physical Basics
www.geographie.ruhr-uni-bochum.de/agklima/vorlesung/strahlung/spektrum-atmosphaere.jpg
– Visible light 0.4 - 0.7 μm
• Reflected solar radiation
• Atmospheric influences particularly on blue and green light
• Several applications, e.g. land use mapping
– Near infrared 0.7 - 3 μm
• Reflected solar radiation
• Nearly no atmospheric influences
• Main application: Classification of
vegetation, forest health survey (healthy green plants
strongly reflect near infrared radiation), classification of
water (expanses of water seem dark as they absorb all)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 721
9.1 Physical Basics
http://altmed.creighton.edu/
– Far infrared (thermal energy) 3 - 1000 μm
(usually : 8 - 14 μm)
• Radiation emitted by the earth
• Nearly no atmospheric influences (but clouds are
impermeable, CO2 as well: greenhouse effect is measurable!)
• Applicable day and night
• Measurements beneath the
surface to some extent
(pipelines and leaks...)
• Applications for which the temperature and its change are
important, e.g. sea temperature, thermal properties of stone,
tectonics
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 722
9.1 Physical Basics
http://www.qualitas1998.net/paul/
– Passive microwaves 1 - 300 mm
• Emitted radiation
• Nearly no atmospheric influences (capable to measure
through clouds)
• Measurements beneath the surface to some extent
• Complex signal difficult to interpret
• Low ground resolution (weak signal)
• Disadvantageous SNR (Signal-to-Noise
Ratio) → noisy images
• Main applications: Meteorology (temperature profiles of
the atmosphere) und oceanography (ice observation)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 723
9.1 Physical Basics
htt
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– Active microwaves (radar) 1 - 300 mm
• Reflected, transmitted microwave radiation
• Nearly no atmospheric influences (except reaction on water drops)
• Applicable day and night
• Measurements beneath the surface to some extent
• Polarization effects
• Higher ground resolution as passive microwaves
• Complex signal
• Doppler effect allows detection of moving objects (military applications), sea pollution
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 724
9.1 Physical Basics
htt
p:/
/ww
w.w
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line.
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• Orbits
– Altitude, orbital period,
– Apogee/perigee
• Greatest/least distance from the earth
– Inclination
• Angular distance of the orbital plane from the equator
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 725
9.1 Physical Basics
v orbital speed
R Earth‘s radius= 6 370 km
g0
gravitational accelerationon the Earth‘s surface = 9,81 m/s2
rradius of the satellite orbit
www.satellitentracking.de/txt/ 04_satellitenbahnen.html
– Low Earth Orbit (LEO)
• Heights between 200 and 600 km
• Manned space stations: low inclination and heights above 400 km
• Satellites with biological or material experiments and astronomical satellites
• Spy satellites 90° inclination , perigee 200-250 km, apogee 600-900km
– Medium Earth Orbits (MEO)
• All orbits above 1000 km up to 36000 km
• Navigation satellite systems (GPS, Glonass)
• Small communication satellites
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 726
9.1 Physical Basics
http://www.tobedetermined.org/
– Geosynchronous/geostationary Orbit (GSO)
• Orbit height approximately 35786 km, 0° inclination
• Period is equal to the Earth's rotational period → It maintains the same position relative to the Earth's surface
• Television satellites, weather satellites
– Sun Synchronous Orbit (SSO) or Polar Earth Orbit (PEO)
• Orbit height between 700 and 1000 km, inclination approximately 90°
• Orbit ascends or descends over any given point of the Earth's surface at the same local mean solar time so the surface illumination angle will be nearly the same every time
• Earth observation satellites
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 727
9.1 Physical Basics
htt
p:/
/cim
ss.s
sec.
wis
c.ed
u/s
age/
• Passive systems: photography, scanner (optic,
mechanical, optoelectronic)
• Active systems: radar sensors
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 728
9.2 Recording Techniques
reflected solarradiation
thermal radiation
reflected artificialradiation
R RT/R
passive systemsactive systems
• Passive technique
• VIS and NIR (400-1000 nm)
• Analog storage medium
• Common types of films
– Black and white/panchromatic:
• Highest geometric resolution
– Infrared
• Unusual representation
• Contrastier
• Distinction between coniferous anddeciduous forests
• Surfaces of water easier to identify
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 729
9.2 Photographic Systems
[Al07]
– Color/chromatic:
• Worse geometric resolution as black and white, better
thematic interpretability
– Color infrared films:
• The blue-sensitive layer is replaced by an emulsion sensitive
to a portion of the near infrared region
• Good thematic interpretability (vegetation).
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 730
9.2 Photographic Systems
[Al07]
• Example: aerial photo of Braunschweig
– Altitude approximately 1600 m
– Ground resolution 10 cm
– Color reversal film
– Central projection
– 21. April 2005
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 731
9.2 Photographic Systems
www.braunschweig.de/.../luftbilder.html
• Example: Cosmos with KVR 1000 Camera
– Russian spy satellite
– Polar, sun-synchronous
– Altitude 200km
– Ground resolution 2m
– Black and white film
– Durability 45 days
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 732
9.2 Photographic Systems
http://www.spotimage.fr/web/en/186-kvr-1000.php
• Disadvantages
– Difficult radiometric calibration
– Low spectral bandwidth
– Analog data
• Advantages
– Relatively cheap
– High resolution
– „Spontaneous“
recording of areas
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 733
9.2 Photographic Systems
http://saturn.unibe.ch/.../Fotogrammetrie-Bildflug.pdf
• Opto-mechanical scanner
• A rotating 45 degree scan mirror continuously scans the Earth beneath the platform perpendicular to the direction of flight
• The system collects data one pixel at a time sequentially
• A scan line (mirror rotation) is equivalent to the image swath
• The forward motion of the platform used to acquire a scene with sequential scan lines
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 734
9.2 Whisk Broom Scanner
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 735
9.2 Whisk Broom Scanner
scan direction
aperture anglealtitude
sensorplatform
flight direction
a: geometric resolution > ground segment
s: swath width
instantaneous field of viewIFOV: pixel
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
motor
rotatingmirror
radiation
optical systemtelescope
beam splitterdispersion prism
photodetectors
beam splitterinterference grid
electronicsamplifier, converter
streamermagnetic tapeHDDT, CCT
• Radiation imaging
– Mirror rotates around an axis parallel to the flight direction
– The radiation is split into its various wavelengths and focused onto detectors
– Stored on magnetic tape (HDDT, CCT), remote data transmission
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 736
9.2 Whisk Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
• Advantages– Precise spectral and radiometric
measurements
– Wide total field of view
– Digital data, remote data transmission
• Disadvantages– Relatively short dwell-time
– S-bend
– Panoramic distortion
– Low SNR → limited radiometric resolution
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 737
9.2 Whisk Broom Scanner
http://landsat.gsfc.nasa.gov/images/archive/c0005.html
• Landsat
– American satellite series
• Landsat 1: 1972-1978
• Landsat 2: 1975-1981
• Landsat 3: 1978-1983
• Landsat 4: 1982-1993
• Landsat 5: since 1984
• Landsat 6: 1993 failure
• Landsat 7: since1999
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 738
9.2 Whisk Broom Scanner
http://de.wikipedia.org/wiki/Landsat
1-3
6, 7
4, 5
– Orbit
• Near polar, sun synchronous
• Altitude: 907-913 km (Landsat 1-3),
705 km (Landsat 4-7 )
• Inclination: 99.2° (Landsat 1-3),
98.2° (Landsat 4-7)
• Orbital period:
approximately 100 minutes
→ 14 circulations per day
• Provide complete coverage
of the Earth every 18
(Landsat 1-3) respectively 16
days (Landsat 4-7)Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 739
9.2 Whisk Broom Scanner
ground trace for Landsat1-3 for one day [Al07]
LANDSAT 4,5 (1-3) LANDSAT 4,5 LANDSAT 7
sensor Multispectral Scanner (MSS)
Thematic Mapper (TM) Enhanced Thematic Mapper Plus (ETM+)
pixel size 79 x 79 m² 30 x 30 m² 30 x 30 m²
spectralchannels
1 (4) 0,50 - 0,60 µm, green2 (5) 0,60 - 0,70 µm, red3 (6) 0,70 - 0,80 µm, near infrared4 (7) 0,80 - 1,10 µm, near infrared
1 0,45 - 0,52 µm, blue-green2 0,52 - 0,60 µm, green3 0,63 - 0,69 µm, red4 0,76 - 0,90 µm, near infrared5 1,55 - 1,73 µm, mid infrared7 2,08 - 2,35 µm , mid infrared
1 0,45 - 0,52 µm, blue-green 2 0,52 - 0,60 µm, green3 0,63 - 0,69 µm, red4 0,76 - 0,90 µm, near infrared5 1,55 - 1,73 µm, mid infrared7 2,08 - 2,35 µm , mid infrared
thermal channel 6 10,4 - 12,5 µm (120 x 120 m²)
6 10,4 - 12,5 µm (60 x 60 m²)
panchromatic channel 8 0,52 - 0,90 µm (15 x 15 m²)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 740
9.2 Whisk Broom Scanner
– Typical combination of channels
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 741
9.2 Whisk Broom Scanner
0,5-0,6 μm 0,8-0,9 μm
false colour composite
0,6-0,7 μm
true colour compositehttp://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
infraredredgreen
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 742
9.2 Whisk Broom Scanner
http://landsat.gsfc.nasa.gov/images/lg_jpg/f0012_77-89-06.jpg
• Optoelectronical scanner
• Employs a linear array of solid semiconductive
elements to acquire one entire line of spectral
data simultaneously
• Scan lines perpendicular to the direction of flight
• Forward motion of the platform to acquire a
sequence of imaged lines to map a scene
• CCDs (charge coupled device) to serialize parallel
analog signals
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 743
9.2 Push Broom Scanner
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 744
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
scan direction
: aperture angle
altitude
sensorplatform
flight direction
a: geometric resolution > ground segment
s: swath width
focal distance
lens
aperture angle
sample mirror
CCD sensors
optical system
radiation
• Radiation imaging– Tilted mirror, sometimes fixed sometimes tiltable
– CCD image sensors in the image plane of the lens: line scan camera
– Data storage in parallel memory chips, remote data transmission
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 745
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
• Spot (Systeme Probatoire d'Observation de la
Terre)
– French satellite series
• Spot-1: 1986-1990
• Spot-2: since 1990
• Spot-3: 1993-1997
• Spot-4: since 1998
• Spot-5: since 2002
– Two identical parallel sensors
that can be operated
independently of one another
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 746
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
http://www.fe-lexikon.info/images/Spot5.jpg
1-3
4
5
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
angledview
nadir-looking
– Pivoting of the sensors can be employed for
stereoscopy and also for a higher repeat circle
– Sensors are operated from the ground stations
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 747
9.2 Push Broom Scanner
http://www.terraengine.com/Dgroundstation.cfm
– Orbit
• Sun synchronous
• Altitude: 822 km
• Inclination 98,7°
• Orbital period 101,4 min
→ approximately 14 circulations per day
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 748
9.2 Push Broom Scanner
SPOT 1-3 SPOT 4 SPOT 5
sensor HRV (Instrument Haute Résolution Visible)
HRVIR (High Resolution Visible and Infrared)
HRG (High Resolu-tion Geometric)
geometricresolution
20 m (XS), 10 m (PN)
20 m (XS), 10 m (P)
10 m (VIS, NIR), 2,5/5 m (PAN), 20 m (MIR)
radiometricresolution
0,5-0,9 μm: 3 VIS, 1 NIR
0,5-1,75 μm: 3 VIS, 1 NIR, 1 MIR
0,45-1,75 μm: 2 VIS, 2 NIR, 1 MIR
http://spot5.cnes.fr/.../35.htm
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 749
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
Spot-1 HRV P-Modus
San Diego(USA), panchromatic, resolution 20 m
Spot-1 HRV XS-Modus
Detroit(USA), false colourcomposite, resolution 30 m
Spot-5 HRG XS-Modus: stereo
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 750
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
Dead sea (Jordan), panchromatic, 11/2002 resolution 2,5 m
• Radio Detection And Ranging
• Principle:
– Transmitting radarpulses (microwaves)and recording thereflected radiation→ active
– The transit timeand the strengthof the reflectedsignal is measured
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 751
9.2 Radar
[LKC08]
• Nadir:
– The local vertical direction pointing in the direction of
the force of gravity at that location
• Range:
– Line of sight
• Azimuth:
– Direction of flight
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 752
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
• Recording parameters
– Polarization
• Direction of the electric field which isperpendicular to the direction ofpropagation in the transmitted radarsignal (H = horizontal, V = vertical)→ 4 possibilities: HH, VV, HV, VH
– Depression angle θd
– Pulse length
– Wavelength is divided into bands
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 753
9.2 Radar
http://ladamer.org/.../FE1-06-Radar.pdf
K-band X-band C-band L-band P-band
0,7-1 cm 2,4-4,5 cm 4,5-7,5 cm 15-30 cm 77-136 cm
B
GR2
GR1
R2
R1
A
A
Bβ
• Azimuth resolution AR depends on beam
width(β) and the ground range distance (GR)
→Azimuth resolution is better in the near range
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 754
9.2 Radar
[LKC08]
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 755
9.2 Radar
[LKC08]
• Ground range resolution (GRR) depends on the
pulse length (τ) and the depression angle(θ)
– Distinction between
A and B only possible
if the pulse passed A
completely before
reaching B
→ Better ground range resolution in the far range
• In order to improve the resolution
– Ground range
• Decrease pulse length
– Azimuth
• Decrease wavelength
• Increase antenna length
• The azimuth resolution
is unacceptably coarse
for systems operating at
satellite altitudes
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 756
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
• Synthetic aperture radar (SAR)
– Scene is illuminated over an interval of time → history of reflections
– The further an objectthe longer the time itis illuminated
– As changes in frequency are systematic separate components of the reflected signal can be assigned to their correct position
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 757
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
• Doppler-effect
– Approaching → increase in
frequency
– Receding → decrease in
frequency
• Physical antenna as small
as possible
• Azimuth resolution
independent of GR and λ
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 758
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
syn
thet
ic a
per
ture
radar pulse with frequency v2
frequency v2
object
v1 – v2 > 0v3 – v2 < 0
• Comparison of the resolution between systems
with real (a) and synthetic (b) aperture
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 759
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
• Interactions between radar signals and materials
very complex as it depends on:
– Wavelength
– Incidence angle
– Electrical properties
– Moisture
– Surface property
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 760
9.2 Radar
http://www.meteo.physik.uni-muenchen.de/.../fe_boden_micro.html
• Penetration depths of microwaves
– Increases with decreasing wavelength
– Decreases with increasing
conductivity, which is also
influenced by moisture
– Is higher for smoother
surfaces
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 761
9.2 Radar
vegetation
dry alluvium
glacier
[Al07]
• Problem-oriented quantitative analysis of radar
images is difficult as it relies mostly on hardly
comprehensible interdependencies
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 762
9.2 Radar
C-Band L-Band P-Bandhttp://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/pdf/bas_intro_e.pdf
• ERS (European Remote Sensing Satellite)
– ERS-1: 1991-1999
– ERS-2: since 1995
– Envisat: since 2002
– Orbit:
• Sun synchronous
• 800 km altitude
• 98,5° inclination
• Orbital period 100 min
• Repeat circle 35 days
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 763
9.2 Radar
http://www.esa.int/esaEO/GGGWBR8RVDC_index_0.html
http://www.raumfahrer.net/raumfahrt/envisat/ablauf.shtml
– Instruments
• SAR two modes of operation: image mode and wave mode in combination with the wind scatterometer (WS)
• WS, active microwaves to measure ocean surface wind speed and direction
• RA (Radar Altimeter); active: Ku-Band (13.8 GHz) measures variations in the satellite‟s height above sea level and ice with an accuracy of a few centimetres
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 764
9.2 Radar
http://ceos.cnes.fr:8100/.../ers/earonc00.htm
• GOME (Global Ozone Monitoring Experiment) Spectrometer
(UV and VIS) provides information on ozone
• ATSR (Along-Track Scanning Radiometer) an Imaging Infrared
Radiometer (IRR: 4 channels, temperature) and a passive
Microwave Sounder (MWS: 2 channels providing measurements
of the total water content of the atmosphere within a 20 km
footprint)
• PRARE (Precise Range and Range Rate Equipment), all-weather
microwave ranging system designed to provide measurements
used for highly precise orbit determination and geodetic
applications, such as movements of the Earth‟s crust
• LRR (Laser-Retroreflector) passive optical device(IR) used as a
target by ground-based laser ranging stations to determine the
precise altitude
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 765
9.2 Radar
• ATSR image of Crete
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 766
9.2 Radar
http://earth.esa.int/earthimages/
• SAR image of Vorpommern
– Three images acquired in September 1991 were overlaid each in one of the primary colors
– Considerable changes of surface structure and moisture due to farming
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 767
9.2 Radar
http://earth.esa.int/earthimages/
• SAR image of the coast of Norway
– In situations with little windmany different featuresappear on the ocean surface
• Linear elements: current shear
• Black areas: very light winds
• Linear features and internalwaves:
currents alternated by the bottom topography, in shallow sea
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 768
9.2 Radar
http://earth.esa.int/earthimages/
• Comparison of the wavelengths used by different
satellites
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 769
9.2 Recording Techniques
http://www.fe-lexikon.info/images/sp_sat.gif
• Light Detection and Ranging
• Active sensor
• Laser beams (UV, VIS near IR) to measure– Distance
– Speed
– Chemical composition and concentrations
• Often imprecisely called "laser-radar"
• LASER (Light Amplification by Stimulated Emission of Radiation)– Device that emits an intense
narrow low-divergence beam of a specific wavelength
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 770
9.2 LIDAR
[SX08]
• Airborne Laserscanning
– The distance between the sensor and the surface to
be measured is determined from the runtime of a light
pulse
– By deflection of the laser beam and the forward
movement of the aircraft a wide strip is scanned
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 771
9.2 LIDAR
elliptical scanning swiveling mirrorfibre scanner
– Parameters
• Sampling rate
• Scan angle
• Scan frequency
• Altitude
• Aircraft speed
• Distance between the trajectories
– Recorded data
• Position
• Orientation of the aircraft
• Angle of every emitted beam
• Measured distance
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 772
9.2 LIDAR
http://www.fht-stuttgart.de/fbv/fbvweb/veranstaltungen/GIS-Day/Rueckblick/gis_day2004_guelch.pdf
– One laser beam might be reflected at different heights,
e.g. in presence of vegetation:
• Primary return: originate from the first objects a lidar pulse
encounters, often the upper surface of a vegetation canopy
• Well suited to create a
digital object model (DOM)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 773
9.2 LIDAR
http://www.fht-stuttgart.de/.../gis_day2004_guelch.pdf
• Secondary returns: lower vegetation layers and the ground surface
• Last return provides data for a digital terrain model (DTM) if the vegetation is not too dense
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 774
9.2 LIDAR
http://publik.tuwien.ac.at/files/PubDat_166922.pdf
emittedpulse
first echo last echo
time
time
time
signalstrength
scrup terrain
discrete echo determination
full waveform digitisation
signalstrength
signalstrength
– Coordinates of the
reflection points:
• Calculated from the
position and orientation
of the sensor (by GPS
and INS), the deflection
angle of the beam and
the distance between
sensor and reflection
point
– Result: 3D point set
along the trajectory
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 775
9.2 LIDAR
http://www.photo.verm.tu-muenchen.de/.../EFE03_Kap23.pdf
– Advantages
• Uniform, dense acquisition of points
• Acquisition of height information for
DOM (with vegetation), as well as
for DTM (without vegetation)
• Accuracy in height between 50 and
15 cm in position1m
• Fast area-wide acquisition
• Active measuring method, nearly
independent of illumination
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 776
9.2 LIDAR
http://www.fht-stuttgart.de/.../gis_day2004_guelch.pdf
– Disadvantages
• Arbitrary points, no structure elements (prominent terrain
points, borders)
• Only single points, interpolation
necessary
• Relatively noisy
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 777
9.2 LIDAR
http://www.fht-stuttgart.de/fbv/fbvweb/veranstaltungen/GIS-Day/Rueckblick/gis_day2004_guelch.pdf
• Comparison between topographic maps and remotely sensed images
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 778
9.3 Image Processing
Properties
Remotely sensed image Topographic map
Mapping not true to scale,image scales are only approximations, additional errors if terrain is uneven
Mapping true to scale, only minor changes due to generalization
Mapping not positional accurate, influenced by sensor alignment, grade, earth curvature, etc.
Mapping positional accurate, only minor changes due to generalization
No parallel projection Orthogonal parallel projection of the earth‘ s surface on the map reference plane
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 779
9.3 Image Processing
Content
Remotely sensed image Topographic map
Communicating information in images
Information coded by graphic symbols
Content defined causally by physical-chemical processes
Content defined conventionally, stipulated map symbols, explained in a legend
High information density, but irrelevant data included
Low information density, but all topographically relevant
Unlimited diversity of forms Limited number of map symbols
Snap shot, contains transient data Contains only topographically stable data
content scale independent, no selection
content scale dependent, reduction of information by generalization
Up to date , short production time Not up to date, long production time,problem of revision
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 780
9.3 Image Processing
Readability and interpretation
Remotely sensed image Topographic map
Varying image quality Uniform map quality
No readability objects have to beinterpreted
Objects are directly readable as they are represented by clearly defined symbols
Ambiguous, as interpretation depends on the interpreter
Unambiguous independent of the user
Real 3d impression possible, if third dimension by stereoscopy captured
No real 3d impression, third dimension may only be coded by symbols
Interpretation scale dependent,resolution determines if objects can be recognized
Readability scale independent, granted by generalization
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 781
9.3 Image Processing
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_18112004.pdf
• Geometric errors, distortions
– Inaccurate position and form of objects
– Causes
• Recording techniques and system
• Relief
• Platform (instability, motion)
• Radiometric errors
– Faulty pixel values
– Causes
• Atmospheric interference
• Topographical effects
• Technical defects (sensors, data transfer)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 782
9.3 Image Processing
http://www.fas.org/irp/
• Goals of geometric corrections
– Represent objects in uniform scale and true geometry (system correction)
– Register overlapped images of a scene from different dates and views (image to image registration)
– Register the image to real world map coordinates (image to map registration)
• The planimetricallycorrected imageis calledorthophoto
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 783
9.3 Geometric Errors
[Al07]
aerial photo, uncorrected corrected → orthophoto
• Relief displacement
– Points above the chosen reference plane are moved
radially away from the center
– Points below the chosen
reference plane are moved radially
towards the center
– Radial displacement is
larger near the border
– Displacement diminishes
at the center
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 784
9.3 Geometric Errors in Photographic Systems
http://homepage.univie.ac.at/.../lba_fe_28102004.pdf
invisible space invisible space
reference plane
side view
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 785
9.3 Geometric Errors in Photographic Systems
[SX08]
• Varying scale
– Mapping scale changes with variations in terrain
– The scale of objects closer to the camera is larger than that of objects being further away
– The mapping of a rectangle that covers a terrace is not a rectangle
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 786
9.3 Geometric Errors in Photographic Systems
higher
lower
Map:constant scale
Aerial photo:varying scale
terrace
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_28102004.pdf
• Capturing a scene (image) takes a certain time
• During the recording time the earth rotates eastward, so that the starting point of the last scan line is further west than that of the first line
• The displacement depends on the relative speed of the satellite and the earth rotation and also on the size of the image
• Example (Landsat 7): – 33,8°S (Sidney)
– Image size: 185 km
→ Offset: 10,82 km(ca 6%)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 787
9.3 Geometric Errors in Scanners
pixel
satellitemotion↓
earth rotation →
http://ladamer.org/Feut/pdf/Kursbegleitung/dbv_vl/dbv_vl_kapitel3.pdf
• Whiskbroom scanner
– The distance between sensor and
terrain increases towards the edges
– Size of scanning spots increases towards
the edges
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 788
9.3 Geometric Errors in Scanners
[Al07]
scan direction
flightdirection
↑
http://ladamer.org/Feut/pdf/Kursbegleitung/dbv_vl/dbv_vl_kapitel3.pdf
– If the angular speed is constant, the image seems to be
increasingly compressed towards the edges
– More elevated surfaces are perpendicular moved away
from the flight direction
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 789
9.3 Geometric Errors in Scanners
[Al07]http://homepage.univie.ac.at/.../lba_fe_28102004.pdf
• Image geometry depends on the depression angle
and the terrain
• Oblique perspective (i.e. side-looking) leads to
relief displacement
– The type and degree of relief displacement in the
radar image is a function of the angle at which the
radar beam hits the ground, i.e. it depends upon the
local slope of the ground
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 790
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/.../bas_intro_e.pdf
• Foreshortening
– Compression of those features in the scene which are
tilted toward the radar
– Foreshortening effects are
reduced with increasing
incident angles
– Maximum when a steep slope
is orthogonal to the radar beam
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 791
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/.../bas_intro_e.pdf
• Radar shadow
– Areas not illuminated by the radar
– Caused by either concave or convex relief features if the slope on the opposite side of the antenna is larger than the depression angle
– Typical in high reliefterrain
– Occur in the down-range direction
– Most prominentwith large incidenceangle illumination
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 792
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/pdf/bas_intro_e.pdf
• Layover
– Occurs when the reflected energy from the upper
portion of a feature is received
before the return from its lower
– The top of the feature will be
displaced, or “laid over” relative
to its base
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 793
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/.../bas_intro_e.pdf
• Instability of the platform (aircraft)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 794
9.3 Geometric Errors
change of flightspeed
pitching change ofaltitude
rolling
yawing
[Al07]
http://wdc.dlr.de/data_products/SURFACE/LCC/diplomarbeit_u_gessner_2005.pdf
• Model-based correction algorithm
– Develop a model for a given recording techniques and
platform that considers all its inherent causes for
distortions
– Parameterize the model to fit the actual conditions
under which the image was taken
– Suitable if the kind and cause of the distortion is
known, as earth rotation, satellite orbit or positional
parameters of the platform
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 795
9.3 Geometric Corrections
• Mathematical function to map the positions of pixels
on the coordinates of the same points in a map
– Independent of the sensor platform, commonly used
– Uses ground control points i.e. features visible on the
image with known ground coordinates
– Assign to each pixel a new position in the reference grid
– Involves the following steps:
I. Choice of a suitable function
(mapping)
II. Coordinate transformation
III. Resampling (interpolation)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 796
9.3 Geometric Corrections
corrected image raw image
e
n cre = f (c,r)
n = f (c,r)
• Radiometric corrections
– Dark pixel subtraction • Assumption: the minimum value of every channel is 0 → for each
channel the smallest measured value is subtracted from every value as it has to be an atmospheric influence, very simplifying
– Radiance to reflectance conversion
• Correction of values by known reflection values for certain surface properties
– Atmospheric modeling
• Develop a complex model for the transfer of EM energy under the atmospheric conditions (e.g. vapor content, ozone, temperature, etc.) to the time the image was taken
– Determining missing pixels or rows by interpolation
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 797
9.3 Image Processing
• Emphasizing structures
– High pass filter
• Noise reduction
(smoothing)
– Low pass filter
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 798
9.3 Image Enhancement
[Al07]
0 -1 0
-1 5 -1
0 -1 0
1 91 9
1 91 9
1 9
1 9
1 91 9 1 9
• Contrast enhancement
– Alters each pixel value in the old image
to produce a new set of values that
exploits the full range of values
– E.g. linear stretching
• Chose a new minimum and maximum value
• Intermediate values are scaled proportionally
g‘(x,y) = g(x,y)⋅ c1 + c2 with c1 =255/[max(g(x,y)) – min(g(x,y))], c2 = -min(g(x,y))
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 799
9.3 Image Enhancement
0
0
255
255http://ivvgeo.uni-muenster.de/Vorlesung/FE_Script/3_2.html
• Assignment of objects, features, or areas to
classes based on their appearance on the imagery
• Distinction between 3 levels of confidence
– Detection: determination of the presence or absence
of a feature
– Recognition: object can be assigned an identity in a
general class or category
– Identification: object or feature can be assigned to a
very specific class
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 800
9.4 Thematic Classification
• Eight elements of image interpretation
– Image tone
• Lightness or darkness of a region within an image
• Refers ultimately to the brightness of an area of ground as
portrayed by the film
• Influenced by vignetting, i.e. the image becomes darker near
the edges
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 801
8.4 Thematic Classification
[Ca07]
– Image texture
• Apparent roughness or smoothness of an image region
• Caused by the pattern of highlighted and shadowed areas
created when an irregular surface is illuminated from an
oblique angle
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 802
8.4 Thematic Classification
[Ca07]
– Shadow
• May reveal characteristics of its size or shape that would
not be obvious from the overhead view alone
• Important clue in the interpretation of individual objects
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 803
9.4 Thematic Classification
[Ca07]
– Pattern
• Arrangement of individual objects into distinctive recurring
forms
• Usually follows from a functional relationship between the
individual features that compose the pattern
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 804
9.4 Thematic Classification
[Ca07]
– Association
• Specifies the occurrence of certain objects or features,
without a strict spatial arrangement
• Identification of a class implies that objects of another class
are likely to be found nearby
– Site
• Refers to topographic position
• E.g. sewage treatment facilities are positioned at low
topographic sites near streams or rivers
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 805
9.4 Thematic Classification
– Shape
• Obvious clue to the identity of objects
• Often shape alone might be sufficient to provide clear
identification
– Size
• Relative size of an object in relation to other objects on the
image provides the interpreter with an intuitive notion of its
scale and resolution
• Can be measured, permit derivation of quantitative
information
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 806
9.4 Thematic Classification
• Classification key
– Provide a pictorial, exemplary representation of the
examined areas or objects
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 807
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_02122004.pdf
spruce
silver fir
douglas firbeech
oak
pine
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 808
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/index_20072008.html
• Multispectral classification
– Ideally every class is defined by a typical multispectral
signature, caused by a statistical distribution of the
pixels of each class → Examination of the pixels of a
multispectral image by mathematical algorithms
• With regard to their homogeneity
• Spatial distribution
– Two types of classifiers
• Unsupervised, autonomous
• Supervised, interactive
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 809
9.4 Thematic Classification
– After parameterization
the multispectral feature
space may be divided
into
• Primary feature spaces
(reflectance, temperature
etc.)
• Linear transformed
feature spaces
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 810
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_25112004.pdf
• Unsupervised classification
– Assignment of pixels to spectral classes without prior knowledge of the existence or names of these classes
– Cluster-algorithms to define spectral classes
– Collateral information is used to define thematic classes a posteriori, e.g.:
• Terrain surveys
• Spectral measurements
• Maps
– Particularly suited to determine spectral properties of relevant thematic classes
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 811
9.4 Thematic Classification
soil
vegetation
water
control limits
Band 5
Band 7
• Supervised classification
– Analytical method to extract quantitative information
– Assumption: every class in the feature space can be
described by a probability distribution
• Distribution assigns to every
pixel the probability that it
belongs to the class in whose
area it is located
• Usually Gaussian distribution
• Number of variables
= number of channels
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 812
9.4 Thematic Classification
http://ladamer.org/Feut/pdf/Kursbegleitung/dbv_vl/dbv_vl_kapitel8.pdf
• Physical basics
– Electromagnetic radiation
– Orbits
• Recording techniques
– Photographic systems (Aerial camera, Cosmos)
– Whiskbroom scanner(Landsat)
– Pushbroom scanner (SPOT)
– Radar (ERS)
– LIDAR (Airborne Laserscanning)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 813
9.6 Summary
• Image processing
– Comparison between remotely sensed images and
topographic maps
– Causes of geometric errors
– Image enhancement
• Thematic classification
– Visual interpretation
– Quantitative image analysis
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 814
9.6 Summary
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 815
9.6 Summary
GIS
objects
recordingtechniques
collect
manage
analyse
display
classification
remote sensing
imageenhancements/ corrections
physics