9 Remote Sensing...9.1 Physical Basics 9.2 Recording Techniques 9.3 Image Processing 9.4 Thematic...
Transcript of 9 Remote Sensing...9.1 Physical Basics 9.2 Recording Techniques 9.3 Image Processing 9.4 Thematic...
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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 695
9 Remote Sensing
http://saturn.unibe.ch/.../Fotogrammetrie-Bildflug.pdf
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• 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 696
9 Remote Sensing
Visualization, Cartography
Spatial Data Management
Collection of Spatial Data
Analysis, Modelling
Functional Components Structural Components
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• 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
– Hydrographic survey
• Aerial survey and survey by remote sensing
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 697
9 Remote Sensing
htt
p:/
/i0
0.i.
aliim
g.co
m/
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• 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
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 698
9 Remote Sensing
http://www.etsu.edu/cas/geosciences/
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– 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 699
9 Remote Sensing
http://www.aero-news.net/
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• 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 700
9 Remote Sensing
http://www.gisdevelopment.net/…/mm063d_155.htm www.maps.google.de
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• System characteristics
– Recording techniques • Radiometric resolution
• Geometric resolution
– Platform • Kind of platform
• Altitude
• Orbit
• Period
– Mission
• Spatial coverage
• Temporal coverage
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 701
9 Remote Sensing
http://www.wdr.de/tv/quarks/ http://www.dlr.de/
http://www.giga.de/
http://www.maritime-technik.de/
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• 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 702
9.1 Physical Basics
electrical field
distance
magnetic field M
E
c speed of light
ν: frequency
λ: wavelength
number of cycles that passes a certain point per second
http://www.fe-lexikon.info/images/ ElektromagnetischeWelle.jpg
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• 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 703
9.1 Physical Basics
htt
p:/
/en
.wik
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• Behavior of electromagnetic waves at interfaces
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 704
9.1 Physical Basics
Reflection
Emission Absorption
Transmission
Scattering
Transmission + Reflection + Absorption = 1
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 705
9.1 Physical Basics
[AS14]
solar radiation
sensor
received signal
scattered light atmospheric absorption
and scattering sky radiation
reflection at the surface scattering at the surface
absorption and reflection in the water (suspended particles)
reflection at the ground
water depth
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– 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 706
9.1 Physical Basics
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– 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 707
9.1 Physical Basics
[AS14]
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• 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 708
9.1 Physical Basics
www.eduspace.esa.int/eduspac e/.../images/03.jpg
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• 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 709
9.1 Physical Basics
http://mynasadata.larc.nasa.gov/images/BB_illustration2.jpg
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• 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 710
9.1 Physical Basics
surface emissivity (8-14 μm)
blackbody 1
water, depending on 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
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• Atmospheric window(s)
– Portion(s) of the electromagnetic spectrum that can
be transmitted through the atmosphere
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 711
9.1 Physical Basics
http://www.geographie.ruhr-uni-bochum.de/agklima/
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– 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
– 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
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 712
9.1 Physical Basics
http://www.samtgemeinde-nord-elm.de/
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– 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 713
9.1 Physical Basics
http://www.forestwatch.sr.unh.edu/
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– 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 714
9.1 Physical Basics
htt
p:/
/ww
w.q
ual
itas
19
98
.net
/pau
l/
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– 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 signal-to-noise ratio → noisy images
• Main applications: Meteorology (temperature profiles of
the atmosphere) and oceanography (ice observation)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 715
9.1 Physical Basics
http://nsidc.org/cryosphere/
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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 716
9.1 Physical Basics
htt
p:/
/ww
w.w
ette
ron
line.
de/
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• Orbits
– Altitude, orbital period,
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 717
9.1 Physical Basics
v orbital speed
R Earth‘s radius= 6 370 km
g0
gravitational acceleration on the Earth‘s surface = 9,81 m/s2
r radius of the satellite orbit
r
gRv 0
v [km/s]
r [km]
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– 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 718
9.1 Physical Basics
http://www.skyandtelescope.com/ http://vro.agriculture.vic.gov.au/
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– 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 719
9.1 Physical Basics
http://www.tobedetermined.org/
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– 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 720
9.1 Physical Basics
htt
p:/
/cim
ss.s
sec.
wis
c.ed
u/s
age/
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– To scale
representation
of the Earth,
LEO, and MEO
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 721
9.1 Physical Basics
MEO
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• Passive systems: photography, scanner
(optomechanical, optoelectronical)
• Active systems: radar sensors
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 722
9.2 Recording Techniques
reflected solar radiation
thermal radiation
reflected artificial radiation
R R T/R
passive systems active systems
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• 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 and deciduous forests
• Surfaces of water easier to identify
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 723
9.2 Photographic Systems
[AS14]
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– 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 724
9.2 Photographic Systems
[AS14]
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• Example: Cosmos with KVR 1000 Camera
– Russian spy satellite
– Polar, sun-synchronous
– Altitude 200km
– Ground resolution 2m
– Black and white film
– Durability 45 days
– Missions 1981–2000
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 725
9.2 Photographic Systems
http://www.spotimage.fr/web/en/186-kvr-1000.php
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• Example: digital
aerial orthophotos
of Braunschweig
– Central projection
– Planimetrically corrected
– 30. March 2014
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 726
9.2 Photographic Systems
https://www.braunschweig.de/
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• 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 727
9.2 Photographic Systems
http://saturn.unibe.ch/.../Fotogrammetrie-Bildflug.pdf
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• Optomechanical 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 728
9.2 Whisk Broom Scanner
http://www.mikroelektronik.fraunhofer.de/
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 729
9.2 Whisk Broom Scanner
scan direction
aperture angle altitude
sensor platform
flight direction
a: geometric resolution > ground segment s: swath width
instantaneous field of view IFOV: pixel
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
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motor
rotating mirror
radiation
optical system telescope
beam splitter dispersion prism
photodetectors
beam splitter interference grid
electronics amplifier, converter
streamer magnetic tape HDDT, 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 730
9.2 Whisk Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
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• 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 731
9.2 Whisk Broom Scanner
http://landsat.gsfc.nasa.gov/images/archive/c0005.html
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• Landsat
– American satellite series
• Landsat 1: 1972-1978
• Landsat 2: 1975-1981
• Landsat 3: 1978-1983
• Landsat 4: 1982-1993
• Landsat 5: 1984-2013
• Landsat 6: 1993 failure
• Landsat 7: since1999
• Landsat 8: since 2013
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 732
9.2 Whisk Broom Scanner
http://de.wikipedia.org/wiki/Landsat
1-3
6, 7
4, 5
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– 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 733
9.2 Whisk Broom Scanner
ground trace for Landsat1-3 for one day [AS14]
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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
spectral channels
1 (4) 0,50 - 0,60 µm, green 2 (5) 0,60 - 0,70 µm, red 3 (6) 0,70 - 0,80 µm, near infrared 4 (7) 0,80 - 1,10 µm, near infrared
1 0,45 - 0,52 µm, blue-green 2 0,52 - 0,60 µm, green 3 0,63 - 0,69 µm, red 4 0,76 - 0,90 µm, near infrared 5 1,55 - 1,73 µm, mid infrared 7 2,08 - 2,35 µm , mid infrared
1 0,45 - 0,52 µm, blue-green 2 0,52 - 0,60 µm, green 3 0,63 - 0,69 µm, red 4 0,76 - 0,90 µm, near infrared 5 1,55 - 1,73 µm, mid infrared 7 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 734
9.2 Whisk Broom Scanner
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– Typical combination of channels
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 735
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 composite http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
infrared red green
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 736
9.2 Whisk Broom Scanner
http://landsat.gsfc.nasa.gov/images/lg_jpg/f0012_77-89-06.jpg
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• Optoelectronical scanner
• Employs a linear array of solid semi- conductive 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 737
9.2 Push Broom Scanner
http://www.fotos.docoer-dig.de/
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 738
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
scan direction
: aperture angle
altitude
sensor platform
flight direction
a: geometric resolution > ground segment s: swath width
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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 739
9.2 Push Broom Scanner
http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
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• Spot (Systeme Probatoire d'Oberservation de la Terre)
– French satellite series
• Spot-1: 1986-1990
• Spot-2: 1990-2009
• Spot-3: 1993-1997
• Spot-4: since 1998
• Spot-5: since 2002
• Spot 6: since 2012
• Spot 7: since 2014
– Two identical parallel sensors that
can be operated independently
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 740
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
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http://www.uni-potsdam.de/.../febasis/febasis06_04-1206.pdf
angled view
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 741
9.2 Push Broom Scanner
http://www.terraengine.com/Dgroundstation.cfm
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– 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 742
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)
geometric resolution
20 m (XS), 10 m (PN)
20 m (XS), 10 m (P)
10 m (VIS, NIR), 2,5/5 m (PAN), 20 m (MIR)
radiometric resolution
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
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 743
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 colour composite, resolution 30 m
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Spot-5 HRG XS-Modus: stereo
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
Dead sea (Jordan), panchromatic, 11/2002 resolution 2,5 m
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• Radio Detection And Ranging
• Principle:
– Transmitting radar pulses (microwaves) and recording the reflected radiation → active
– The transit time and the strength of the reflected signal is measured
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 745
9.2 Radar
[LKC15]
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• 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 746
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
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• Recording parameters
– Polarization
• Direction of the electric field which is perpendicular to the direction of propagation in the transmitted radar signal (H = horizontal, V = vertical) → 4 possibilities: HH, VV, HV, VH
– Depression angle θd
– Pulse length
– Wavelength is divided into 5 bands
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 747
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
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B
GR2
GR1
R2
R1 A
A
B β
• 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 748
9.2 Radar
[LKC15]
GRARL
and
L: antenna length
λ: wavelength where
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• 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
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 749
9.2 Radar
Pulse length τ
Front of return wave from A
Front of return wave from B
A B
τ 2
<
Rear of outgoing wave
cos2
cGRR
[LKC15]
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• 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 750
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
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• Synthetic aperture radar (SAR)
– Scene is illuminated over an interval of time → history of reflections
– The further an object the longer the time it is 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 751
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
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• 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 752
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
syn
thet
ic a
pe
rtu
re
radar pulse with frequency v2
frequency v2
object
v1 – v2 > 0 v3 – v2 < 0
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• Comparison of the resolution between systems
with real (a) and synthetic (b) aperture
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 753
9.2 Radar
http://ladamer.org/Feut/studium/fe1/FE1-06-Radar.pdf
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• 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 754
9.2 Radar
http://www.meteo.physik.uni-muenchen.de/.../fe_boden_micro.html
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• 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 755
9.2 Radar
vegetation
dry alluvium
glacier
[AS14]
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• 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 756
9.2 Radar
C-Band L-Band P-Band http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/pdf/bas_intro_e.pdf
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• Sentinel-1
– SAR satellites in the Copernicus program
– Sentinel-1A launched on 3 April 2014, Sentinel-1B on 25 April 2016
– Orbit
• Sun synchronous
• 693 km altitude
• 98.2° inclination
• Orbital period 98.5 min
• Repeat circle 6 days (two satellites)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 757
9.2 Radar
http://104.131.251.97/copernicus/
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– Mass: 2300 kg (including 130 kg fuel)
– Size: 2.8 m long, 2.5 m wide, 4 m high
with 2×10 m-long solar arrays and a
12 m-long radar antenna
– Solar array average
power: 5900 W
– Battery capacity: 324 Ah
– Azimuth resolution:
5, 20, 40 m
– Ground range resolution: 5, 20 m
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 758
9.2 Radar
http://104.131.251.97/copernicus/
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• Image of Ireland
(May 2015)
– Blue: strong changes in
bodies of water or
agricultural activities
within 12 days
– Yellow: urban centers
– Green: vegetated fields
and forests
– Red and orange: bare
soil and rocks
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 759
9.2 Radar
htt
ps:
//d
irec
tory
.eo
po
rtal
.org
/
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• Nepal earthquake displacement
– Image shows how and where the land uplifted and
sank from
the 7.8-
magnitude
earthquake
that struck
Nepal on
25 April
2015
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 760
9.2 Radar
http://www.esa.int/
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• Map of Greenland ice sheet
velocity
– January–March 2015
– About 1200 radar scenes
were used
– Colour scale in meters per
day
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 761
9.2 Radar
http://www.esa.int/
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• 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"
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 762
9.2 LIDAR
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• 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 763
9.2 LIDAR
elliptical scanning swiveling mirror fibre scanner
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– Parameters
• Sampling rate
• Scan angle
• Scan frequency
• Altitude
• Aircraft speed
– 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 764
9.2 LIDAR
https://www.e-education.psu.edu/geog481/
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Last return (DTM)
Primary return (DOM)
– 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 765
9.2 LIDAR
http://www.fht-stuttgart.de/.../gis_day2004_guelch.pdf
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• 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 766
9.2 LIDAR
http://publik.tuwien.ac.at/files/PubDat_166922.pdf
emitted pulse
first echo last echo
time
time
time
signal strength
scrup terrain
discrete echo determination
full waveform digitisation
signal strength
signal strength
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– 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 767
9.2 LIDAR
http://www.photo.verm.tu-muenchen.de/.../EFE03_Kap23.pdf
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– 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 768
9.2 LIDAR
http://www.fht-stuttgart.de/.../gis_day2004_guelch.pdf
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– 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 769
9.2 LIDAR
http://www.fht-stuttgart.de/fbv/fbvweb/veranstaltungen/GIS-Day/Rueckblick/gis_day2004_guelch.pdf
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– Reconstruction of buildings from airborne LIDAR
point clouds is still subject of research
• Building polyhedral models by intersecting detected planes
• Bottom-up reconstruction using a given number of building
parts
• Top-down
statistical
reconstruction
of building roofs
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 770
9.2 LIDAR
[HBS11]
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• Comparison between remotely sensed images and topographic maps
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 771
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
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 772
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
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 773
9.3 Image Processing
Readability and interpretation
Remotely sensed image Topographic map
Varying image quality Uniform map quality
No readability, objects have to be interpreted
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
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 774
9.3 Image Processing
Visual comparison
Remotely sensed image Topographic map
[AS14]
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• 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 775
9.3 Image Processing
http://www.fas.org/irp/
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• 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 planimetrically corrected image is called orthophoto
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 776
9.3 Geometric Errors
[AS14]
aerial photo, uncorrected corrected → orthophoto
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• Radial displacement
– Causes objects to be displaced outward from the nadir
– Increases with the height of the object and distance from the nadir
– E.g. tops of buildings are displaced outward relative to the bases
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 777
9.3 Geometric Errors in Photographic Systems
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 778
9.3 Geometric Errors in Photographic Systems
[SX08]
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• 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 779
9.3 Geometric Errors in Photographic Systems
higher
lower
Map: constant scale
Aerial photo: varying scale
terrace
http://homepage.univie.ac.at/thomas.engleder/
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• 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
• Displacement depends on the relative speed of the
satellite, the earth rotation, and the size of the image
• Example (Landsat 7):
– 33.8°S (Sidney)
– Image size: 185 km
→ Offset: 10.82 km (~ 6%)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 780
9.3 Geometric Errors in Scanners
pixel satellite
motion↓ earth rotation →
http://ladamer.org/Feut/pdf/Kursbegleitung/
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• 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 781
9.3 Geometric Errors in Scanners
[AS14]
scan direction
flight direction
↑
http://ladamer.org/Feut/pdf/Kursbegleitung/ dbv_vl/dbv_vl_kapitel3.pdf
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– 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 782
9.3 Geometric Errors in Scanners
[AS14] http://homepage.univie.ac.at/.../lba_fe_28102004.pdf
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• 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
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 783
9.3 Geometric Errors in Radar Systems
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• Foreshortening
– Compression of those features in the scene which are
tilted toward the radar
– Foreshortening effects are
reduced with increasing
incident
angles
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 784
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/.../bas_intro_e.pdf http://www.geoinformation.net/
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• 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 relief terrain
– Occur in the down- range direction
– Most prominent with large incidence angle illumination
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 785
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/resource/tutor/gsarcd/pdf/bas_intro_e.pdf
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• 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 786
9.3 Geometric Errors in Radar Systems
http://www.ccrs.nrcan.gc.ca/.../bas_intro_e.pdf http://history.nasa.gov/
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• Instability of the platform (aircraft)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 787
9.3 Geometric Errors
change of flight speed
pitching change of altitude
rolling
yawing
[AS14]
http://wdc.dlr.de/data_products/SURFACE/LCC/diplomarbeit_u_gessner_2005.pdf
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• Model-based correction algorithm
– Develop a model for a given recording technique 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 788
9.3 Geometric Corrections
htt
p:/
/ww
w.d
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chw
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/
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• 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 (GCPs) i.e. features visible on the image with known ground coordinates
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 789
9.3 Geometric Corrections
corrected image raw image
e
n c r e = f (c,r)
n = f (c,r)
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– Assigns to each pixel a new position in the reference
grid
– Needs 6 GCPs for two-dimensional second order
polynomials
(12 unknowns)
– 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 790
9.3 Geometric Corrections
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• Example: image to image geocorrection – Matching the coordinate systems or column and row
systems of two digital images
– One image acting as a reference image and the other as the image to be rectified
• Reverence image – Satellite imagery from GoogleMaps
• Input image – Mathematically
distorted reference image
9.3 Image Rectification
791 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Reference
image
9.3 Image Rectification
792 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Mathematical distortions
– Central projection
– Change of altitude
– Pitching
– Rolling
– Yawing
9.3 Image Rectification
793 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Distorted
image
9.3 Image Rectification
794 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Ground control point (GCP)
– Need to be accurately located on the image, e.g.
highway crossings, building corners
– Should be well distributed on the
reference and the distorted image
– Number of necessary GCPs depends
on the function used for rectification
– Can be used to determine the
quality of the rectification, if more
GCPs than needed are defined
9.3 Image Rectification
795 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
+
+
+
+
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• Reference
image with
ground
control points
9.3 Image Rectification
796 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Distorted
image with
ground
control points
9.3 Image Rectification
797 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Mapping functions
– Polynomials are often used
• Degree 1 needs 3 GCPs
• Degree 2 needs 6 GCPs
• Degree 3 needs 10 GCPs
9.3 Image Rectification
http://en.wikipedia.org/wiki/Polynomial 798 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Polynomial of
degree 1
9.3 Image Rectification
799 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Polynomial of
degree 2
9.3 Image Rectification
800 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Polynomial of
degree 3
9.3 Image Rectification
801 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• Reference
image
9.3 Image Rectification
802 Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig
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• 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
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 803
9.3 Image Processing
http://www.spacegrant.montana.edu/
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– 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 804
9.3 Image Processing
http://www.windows2universe.org/
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• Emphasizing structures
– High pass filter
• Noise reduction
(smoothing)
– Low pass filter
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 805
9.3 Image Enhancement
0 -1 0
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• 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)+c2)⋅ c1 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 806
9.3 Image Enhancement
0
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255 http://ivvgeo.uni-muenster.de/Vorlesung/FE_Script/3_2.html
VV
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• Example:
aerosols over
northern India
and Bangladesh
(redmin= 12,
redmax= 200,
greenmin = 20,
greenmax= 196,
bluemin= 0,
bluemax= 170)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 807
9.3 Image Enhancement
htt
p:/
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• 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 808
9.4 Thematic Classification
http://www.bing.com/maps/
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• 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 809
9.4 Thematic Classification
[CW11]
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– 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 810
9.4 Thematic Classification
[CW11]
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– 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 811
9.4 Thematic Classification
[CW11]
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– 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 812
9.4 Thematic Classification
https://jameystillingsprojects.com/ ©1980-2017 Jamey Stillings, All Rights Reserved
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– 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 813
9.4 Thematic Classification
http://maps.google.de/
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– 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 814
9.4 Thematic Classification
http://maps.google.de/
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• Classification key
– Provide a pictorial, exemplary representation of the
examined areas or objects
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 815
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_02122004.pdf
spruce
silver fir
douglas fir beech
oak
pine
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 816
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/index_20072008.html
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• 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 817
9.4 Thematic Classification
http://www.gepdata.ch/
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– 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 818
9.4 Thematic Classification
http://homepage.univie.ac.at/thomas.engleder/lba_fe/lba_fe_25112004.pdf
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– Example:
multispectral image
• Water, soil, vegetation
• λ1 : blue, λ2 : green, λ3 : red
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9.4 Thematic Classification
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• 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 820
9.4 Thematic Classification
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– Example:
classification with iterative k-means clustering
(k=3 was chosen)
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9.4 Thematic Classification
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– Example:
aerial photo
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 822
9.4 Thematic Classification
http://www.koppfoto.de/
k-means (k=2)
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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 823
9.4 Thematic Classification
http://ladamer.org/Feut/pdf/Kursbegleitung/dbv_vl/dbv_vl_kapitel8.pdf
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• Physical basics
– Electromagnetic radiation
– Orbits
• Recording techniques
– Photographic systems (Cosmos, Aerial camera)
– Whiskbroom scanner (Landsat)
– Pushbroom scanner (SPOT)
– Radar (Sentinel-1)
– LIDAR (Airborne Laserscanning)
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 824
9.5 Summary
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• Image processing
– Comparison between remotely sensed images and
topographic maps
– Causes of geometric errors
– Image rectification
– Image enhancement
• Thematic classification
– Visual interpretation
– Quantitative image analysis
Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 825
9.5 Summary
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Spatial Databases and GIS – Karl Neumann, Sarah Tauscher– Ifis – TU Braunschweig 826
9.5 Summary
GIS
objects
recording techniques
collect
manage
analyse
display
classification
remote sensing
image enhancements/ corrections
physics