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TRANSCRIPT
State-of-the-art within
radiometric correction of
large-format aerial
photogrammetric images
Eija Honkavaara, Finnish Geodetic Institute
fagdag Omløp 8.mai 2014
Finnish Geodetic Institute (FGI)
• A research and expert institute that carries out research and development for spatial data infrastructures.
• 4 research departments
• 100 employees
2
Director General
Prof. Jarkko Koskinen
Geodesy and geodynamics
Prof. Markku Poutanen
Geoinformatics and cartography
Prof. Tapani Sarjakoski
Fotogrammetry and remote sensing
Prof. Juha Hyyppä
Navigation and positioning
Prof. Ruizhi Chen
Administration and finances
Ms. Päivi Koponen
Contents
• Introduction
• Theoretical background
• Radiometric sensor calibration
• Radiometric processing
• EuroSDR project 2008-2011
• Examples
• Tree species classification with rigorous radiometric processing
• NDVI time series in Catalonia
• Impact of solar elevation in photogrammetric processes
• Conclusions
3
Introduction • The digital large-format photogrammetric sensors provide excellent
radiometric potential: • Great image quality: Linearity, High dynamic range
• Multispectral data: Photogrammetric remote sensing
• Proper utilization of radiometry provides significant advantages for photogrammetric processes
• Efficient image post-processing
• More flight hours (lower solar altitude)
• Improved visual image quality
• Reflectance calibration
• Improved performance and automation potential of conventional applications
• New quantitative applications, high automation level: classification, monitoring, change detection, …
• Rigorous radiometric processing is a new issue in photogrammetric processing lines and requires new developments
4
Digital photogrammetric cameras DMC
ADS
UltraCam
Microsoft, 2007
Leica Geosystems, 2007
Intergraph, 2007
Intergraph, 2012
DMC2
5
Other relevant airborne imaging
sensors
6
G
R
G
R
B
G
B
G
G
R
G
R
B
G
B
G
Trimble DSS
Small-format customer camers Hyperspectral sensors
© SPECIM Oy Applanix, 2007
Oblique cameras
e.g. Leica
RCD30 Oblique
Penta Pod
DMC orthophoto mosaic
Factors influencing DN
Atmosphere
Illumination
Object
System
7
Theoretical background
8
Definitions • Radiometry: the measurement of
optical radiation in wavelength
range of 0.1-1000 μm
• Radiance
• Measure of the quantity of radiation that
falls from a surface within a given solid
angle in a specified direction
• SI-unit [W m-2 sr-1]
• Photometry: the measurement of
light weighted by the response of
the human eye
• Quantitative remote sensing: Image
DNs are transformed to reflectance
signatures and quantitatively
analyzed
9
Spectral band Wavelength
range
Visible (v) 0.4-0.7 μm
Near infrared (NIR) 0.7-1.1 μm
Short Wave Infrared
(SWIR)
1.1-2.5 μm
Mid Wave Infrared
(MWIR)
3.0-5.0 μm
Thermal or LongWave
Infrared (TIR or LWIR)
8.0-14 μm
At-sensor radiance
Lat_sensor = Ls + Lsky + Lbg+ Lbg_multi+Ladj + Latm.
Ls : direct sun illumination Lsky : Skylight Lbg : Background Lbg_multi: Background multiple Ladj : Adjacency effect Latm : Atmospheric path radiance
/cos)()(),,,,(),,,,( 0
svsrriirriis EL
At-sensor radiance
Direct sun component
At-sensor radiance: radiance
entering to the imaging system
Radiation components
• MODTRAN4 at-sensor
radiance simulations:
• 0.3 reflectance target
• 500 m and 1500 m flying heights
• Effect of skylight and path
scattered radiance decreases
towards longer wavelengths and
with increasing flying altitude
1500 m
500 m
Total Radiance
Path Scattered (C)
Single Scattered Path Rad. (B)
Ground Reflected (A+B+D+E+F)
Direct reflected (A)
11
Atmospheric influences • Scatterig
• Rayleigh scattering • On particles much smaller than light wavelength (air molecules, aerosols).
• Symmetric in forward and backward directions.
• Power of scattered radiation proportional to with λ-4.
• Causes the blue color of the sky.
• Mie scattering • On particles of the size of the light wavelength, i.e. aerosols, dust particles, sea salt.
• Strongly in forwad direction.
• Varies slowly with wavelength
• Non selective scattering. On partilces greater than light wavelength, e.g. rain drops, snow. Varies neither with wavelength nor scattering angle.
• Gaseous absorption, mainly due to water vapour, carbon dioxide, oxygen
• Atmospheric influences are typically estimated by using radiative transfer codes, mainly Modtran 5 or 6S
12
Object properties – Reflectance Reflectance as the function
of the wavelength
13
• Reflectance ρ = dФr/dФi
• Ratio of radiant exitance (M
[Wm-2]) to the irradiance (E
[Wm-2])
• Law of energy conservation -
> ρ [0,1]
• Reflectance factor R =
dФr/dФid
• Ratio of radiant flux reflected
by a surfacte to that reflected
by an ideal and diffuse
(Labmbertian) surface
(similar irration and reflection
conditions )
Object properties – reflectance anisotropy
φi
i
Light
source
Observer
North
φr
r
),(
),,,(),,,(
ii
rriirrii
E
L
14
BRDF
L(θi,φi,θr,φr) Reflected radiance
E(θi,φi) Irradiance
• BRDF – Bidirectional reflectance distribution function
• Observed reflectance is dependent on viewing and illumination directions
• Influences are the largest in the solar principal plane: the plane containing the illumination source (sun), object, and observer
• With photogrammetric cameras FOVs are +/-30°: the anisotropy has to be taken into account
• Object specific
Sources of anisotropic reflectance • Specular reflectance
• Hot spot
• Sunglint reflectance on rough water surfaces
• Leaf/vegetation reflectance (volume scattering)
• Shadow-driven reflectance (surface scattering, gap driven)
• Microscopic backscattering on some surfaces (e.g. rocks)
15
Beisl and Woodhouse 2004
16
17
Central radiometric quality indicators of
images
• Spectral sensitivity: Which wavelengths are observed?
• Spectral resolution: How many channels? Width of the channels?
• Radiometric resolution: How many grey levels?
• Dynamic range: How dark and bright objects? • Signal-noise-ratio (SNR), Saturation
• Point spread function
• Grey value histogram
• Stability
• Calibration quality
• Traceability
18
Radiometric sensor calibration
19
Sensor/camera model (1) platform altitude
platform motion
at-sensor
radiance sensor imaging
optics
detectors electronic
s
A/D DN
spectral filters
or
dispersion
element
),(4
)(),(
2
0 yxLN
yxE i
max
min
),()(),(
dyxERyxs i
bb
ddyxPSFsyxe bb ),(,),( max
min min
max
)),(int( bbbpb offsetyxegainDN
),( yxL
Sensor model (2)
0
)()( dSLGADN sensoratd
DNf
KL
number
2'
The DN value of a given pixel, after dark pixel substraction
is applied:
Where
G system gain,
Ad area of detector,
Ω lens solid angle (aperture),
τ integration or exposure time,
S(λ) system level spectral response,
λ wavelength
Band averaged at-sensor radiance
21
Radiometric system
model: transformation
between the at-sensor
radiance and DN
Radiometric calibration - parameters • Relative pixel wise calibration: normalize output
of all detectors to the similar level and eliminate systmatic errors (noise in space)
• Photo Response Non-uniformity (PRNU): Sensitivity differences of individual CCD-elements
• Lens fall-off: Due to the drop of light intensity towards the edges of the focal plane
• cos4-law Eθ = E0 cosnθ
• Dark signal (DS), Dark signal Non-uniformity (DSNU)
• Uncorrected errors contribute to the signal noise
• Absolute calibration: relationship between the incoming radiance and DN
• Spectral response with respect to wavelength: central wavelength, badwidth
• Calibration methods: Laboratory, In-flight, Vicarious
DNf
KL
number
2'
22
Spectral response of photogrammetric
cameras DMC
0.0
0.2
0.4
0.6
0.8
1.0
350 450 550 650 750 850 950 1050
Wavelength
PAN
R
G
B
NIR
UltraCamD
0
4
8
12
16
20
350 450 550 650 750 850 950 1050
Wavelength
PAN
R
G
B
NIR
DMC II
23
ADS
Spectral response of some satellite
sensors and ADS
24
Laboratory calibration, ADS40 as example
• Absolute and relative: NIST traceable calibrated, homogeneous and isotropic light source illuminating the entire lens over the full aperture -> Integrating sphere
• Spectral: NIST tracable monochromator with a broad band light source and a spectrogrph to provide light with narrow spectral bandwidth
• Dark current: • Laboratory: black image
• Flight: Imaging with closed shutter, using black pixels in CCD
• NIST: National institute of Standards and Technology
U. Beisl, 2006: ADS 40
25
Why vicarious radiometric cal/val?
• ” The requirement for accurate radiometry is a thorough
understanding of the measurement problem, a complete
description and understanding of the instruments, and
mechanisms for comparing and assessing results “
(Johnson et al. 2004)
• Is laboratory calibration valid in flight conditions?
• What is quality of output products of an imaging system
(reflectance calibration etc.)?
• System cal/val after updates and in beginning of season
• Tuning image collection process, e.g. exposure
• Continuous change of systems
26 26
Photogrammetric process vs. imaging
system
Image product generation system
Referencing
Radiometric
correction
Georeferencing
Restoration
GIS
Image acquisition
Mission
design
Post-processing Image collection Mission
preparation
Measurement and
interpretation
Measurement
Interpretation
Calibration
Image acquistion system
27
Vicarious radiometric
cal/val
d1_g5: H = 500 m
0
500
1000
1500
2000
2500
3000
3500
4000
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18
At-sensor radiance
RA
W D
N
PAN
R
G
B
NIR
28
Requirements
•Accurate reflectance targes
•Reflectance characterization of targets
•Atmospheric observations
•Software for radiative transfer
Traceable Reflectance measurement chain
29
Laboratory at Metrology Research
Institute of Aalto University
MetEOC: Metrology for Earth Observation and Climate
CEOS test site catalog,
radiometry sites
30
Use of different calibration methods
Parameter Lab In-flight Test-field
Relative radiometry x x
Light falloff x x
Dark current x x
Spectral response x
Absolute radiometry (sensor) x x
Absolute radiometry (system) x
System validation x
31
Radiometric calibration of different
photogrammetric sensors • ADS40: NIST traceable calibration (Beisl 2006)
• Relative calibration: PRNU, Light falloff
• Dark current, DSNU
• Absolute calibration: Gain, offset
• Spectral response
• DMC : NIST traceable calibration (Pagnutti et al. 2009)
• Relative calibration: PRNU, Light falloff
• Dark current, DSNU
• Absolute calibration: Gain, offset
• Spectral response
• UltraCam (Calibration protocol)
• PRNU,defect pixels, light falloff
• Methods are still developing
32
Radiometric processing
33
Reflectance images by physical
correction method In typical photogrammetric imaging conditions, radiance entering
the sensor
Lat_sensor = Ls + Lsky + Latm
34
ρ=π(Lat-sensor - Latm)(1-sρ
)/(S τs τv)
s Spherical albedo of atmosphere, i.e. the fraction of the upward
radiance that is backscattered by the atmosphere
ρ
Average reflectance of the surrounding area
1-sρ
Term to take into account the multiple scattering
S Mean solar irradiance in surface S=Eλ0cosθs
τs τv Transmittance in solar and viewing paths. Derived using radiative
transfer models and image information
Reflectance
Reflectance images by Empirical line method
• Parameters are determined using 2 or more reference targets
• Artificial panels or natural surfaces measured by spectrometer
• Sometimes also spectral libraries are used to provide reference values of typical targets
• Dark vegetation is commonly used as dark reference
• Simple and functional method if reference targets are available in the campaign area.
• Commonly used in remote sensing
• In photogrammetric mapping projects this is not typically situation.
• Parameters are valid only for the specific campaign conditions
35
ρ = c1´ DN + c0´
Shadow correction
• Types of shadows: cast shadow and self shadow
• Shadowing object can be opaque or transparent, resulting in full
(uniform, e.g. building) shadow or partial (non-uniform, e.g. tree)
shadow.
• No definite boundary between shadowed and non-shadowed
regions.
• Cloud shadows
• Shadow detection
• Using a 3D surface model
• Using image information: e.g. ATCOR-ADS : signal in blue spectral
band is much higher (Schläpfer 2012)
36
Additional corrections
• BRDF-correction: Correction of directional reflectance
effects to obtain uniform image mosaics etc.
• Class specific BRDF-correction needed
• Changing illumination conditions
• Adjacency effects
• Topographic correction
37
ATCOR4: Atmospheric correction of airborne
multi- and hyperspectral images • ATCOR-4: Atmospheric & Topographic Correction for
wide FOV airborne optical scanner data
• Based on large “monochromatic” atmospheric database for altitudes between 1-20km, compiled with the MODTRAN5 –code.
• Basic output is surface reflectance cube i.e. image with the original image-DN’s converted to surface reflectances. Options
• Correction of topographic effects
• Empirical, class-specific BRDF correction
• De-shadowing of cloud/and other shadow areas
• Statistical haze removal
• In-flight sensor calibration using ground reference targets
• Algorithm for automatic classification of reflectance spectra
• Calculation of value adding products: SAVI, LAI, FPAR, albedo, surface energy fluxes
38
ATCOR4: Requirements for
atmospheric correction • Accurate radiometric calibration of the sensor
• Either from the sensor manufacturer or by performing in-flight calibration using 1, 2 or >2 ground reference reflectance targets
• An accurate estimation of the main atmospheric parameters • Aerosol type: rural, urban, maritime, desert
• Can be estimated in ATCOR based on images, if image contains dark vegetation
• Visibility (or aerosol optical thickness)
• User given, interactive estimation or automatic calculation (requires red/NIR –channels and dark targets)
• Water vapor
• Automatic calculation or estimation based on the season
39
Leica ADS40 and XPro • The first reflectance calibration in photogrammetric post-processing software.
• Correction is based on high quality, calibrated sensor, physical/empirical
atmospheric modeling and image data.
• Fulfils the usability requirements of mapping agencies: fully automatic,
ground reference targets are not needed.
• Prosessing in XPro
• At-sensor radiance by laboratory calibration DN -> Lat-sensor
• Directional reflectance
• Atmospheric reflectance is based on dark pixel method. Unknowns : L0,τup,
τdown and s are solved by regression based on simulated atmospheric data.
The explicit form of the regression functions have not been published.
• BRDF-correction to eliminate object anisotropy
• Similar method has been implemented for DMC and RCD30 frame
images, available soon
)1()( 0
s
S
LL
downup
sensorat
40
Image from U. Beisl
Beisl, U., Telaar, J., Schönemark, M. v., 2008. Atmospheric correction,
reflectance calibration and BRDF correction for ADS40 image data.
ISPRS Peking Symposium, VII, WG VII/1.
41
Radiometric block adjustment, Pepita
of IGN France
• A method for equalising a block of digital aerial images.
• Can be considered as relative radiometric calibration.
• Based on a parametric, semi-empirical radiometric model taking into account BRDF, haze differences between blocks of images, solar elevation, sensor settings (exposure) and chromatic aberrations.
• Does not require calibrated sensor
• The model parameters are computed through a global least-squares minimisation process, using radiometric tie-points in overlapping areas between the images.
• The method has been used in the IGN/France orthoimage workflow since 2005. Fully operational.
• Pepita type method has been implemented in UltraCam post-processing software UltraMap
42
IGN/France Pepita model
• Unknowns
• For each image: k, e, a
• For each image block collected in similar conditiosn: Lhaze
• Radiometric tie points are collected over the block area.
Minimization task
43
Solar
irradiance
BRDF-model
Radial term to
correct chromatic
aberration
Exposure
time
Hazeterm Visibility and
solar elevation
Ground
reflectance
Observed
at-sensor
radiance
Chandelier, L.; Martinoty, G. Radiometric aerial
triangulation for the equalization of digital aerial
images and orthoimages. Photogramm. Eng. Remote
Sens. 2009, 75, 193-200.
FGI’s radiometric block
adjustment for frame images
• Data • Overlapping spectral rectangle format data cubes
• Tasks • Eliminate radiometric disturbances caused by sensor instability and
illumination/atmosphere
• BRDF compensation
• Reflectance calibration
• Approach • Radiometric model parameters using radiometric block adustment with a
network of radiometric tie points
• Optional insitu irradiance measurements
• Reflectance images using reflectance targets
44
F
D A A
B
C E G
O
φi
i
Light
source
Observer
North
φr
r
Radiometric end-products of digital
photogrammetric cameras • Radiometric end products: Reflectance image, true color image,
relatively radiometrically corrected image, something else • ADS40: Chain for reflectance image generation
• NIST traceable laboratory calibration • Xpro: processing of line scanner images
• Atmospheric correction: path radiance, adjacency effect • Reflectance calibration • Semi empirical BRDF correction
• DMC: • NIST traceable laboratory calibration • PPS: Radiometric correction of frame images, similar to Xpro (soon available!)
• UltraCamD • Laboratory calibration: relative • Model based radiometric equalization, similar to Pepita
• New, independent approaches are under development: • ATCOR-4 for ADS • ICEE • Pepita of IGN • FGI radiometric block adjustment • …
45
EuroSDR project
46
47
EuroSDR project: ”Radiometric aspects of
digital photogrammetric airborne images”
• Schedule: 2008-2011
• Objectives • Improve knowledge on radiometric aspects of digital photogrammetric
cameras
• Review existing methods and procedures for radiometric image improvements
• Compare and share operative solutions through a comparison of these techniques on a same test data set
• Analyse the benefit of radiometric calibration in order to open new applications (classification, quantitative remote sensing, change detection etc.)
• Headed by • FGI, Finland: Eija Honkavaara, Lauri Markelin
• ICC, Spain: Roman Arbiol
47
EuroSDR project
• Phases
1.Review
2.Empirical research
• Participants
• European National Mapping Agencies (6)
• Software development (1)
• Universities and research organizations (8)
• Many different points of view are covered
48
EuroSDR Review: Major conclusions
• Improvements are requested for the entire process: sensors,
calibration, data collection, data post-processing and data utilization.
• Fundamental problems:
• Insufficient information of radiometric processing chain
• Inadequate radiometric processing lines
• Missing standards (methods, calibration, targets, terminology)
• The basic radiometric end products requested by image users: true
color and reflectance images.
• Expected benefit of more accurate radiometric processing:
• more automatic and efficient imagery post-processing
• better visual image quality
• more accurate, automatic interpretation , remote sensing use
• Only one participant had implemented theoretically based radiometric
correction in 2009. Situation changing since 2011
49
Stakeholders
Type Sensor manufacturing Software
development Data collection Image products Applications Research
U1 x x x x x (x)
U2 x x x x (x)
U3 x x x (x)
U4 x x (x)
U5 x (x)
P1 x x x (x)
P2 x x x x (x)
P3 x (x)
P4 x x (x)
R1 x
SW1 x (x)
M1 x x (x)
Data users perspective:
•Different: sensors, calibrations, processes, settings…
•Changing: sensors, calibrations, processes, settings…
•Subjective selections, non-transparent process….
Sensor Software Applications Image
products
Data
Collection
50
Phase 2 – Materials • Comprehensive campaigns in 2008 with
atmospheric observations and reflectance targets
• DMC + CASI: Banyoles Spain • Flying heights: 820, 1125, 2250 and 4500 m.
• DMC: Sjökulla, Finland • Flying heights: 500 m
• ADS40: Hyytiälä, Finland • Flying heights: 1000, 2000, 3000 and 4000 m • Forest test site
51
Reference targets, Hyytiälä Finland
4 tarps, sand, gravel, weeds, grass
Reflectance factors by ASD Field
Spec Pro FR + 12’’ Spectralon
52
53
Evaluated radiometric correction
methods
• Reflectance images
• Leica Geosystems Xpro
• ReSe ATCOR-4
• Empirical line
• Relative block adjustment
• IGN Pepita
54
Accuracy assessment
• Reflectance error in reflectance units
Erefl=ρdata – ρref
• Reflectance error in %
Erefl%=100(ρdata – ρref)/ ρref
• RMSE
RMSErefl% = √(∑(Erefl%2 )/n)
• Anisotropy scaling
ρdata_nadir = ρdata ρlab_nadir/ρlab_exact
55
Reflectance spectrums of
asphalt and grass by XPro
0
0.05
0.1
0.15
0.2
0.25
400 500 600 700 800 900
Re
fle
cta
nce
Wavelength [nm]
Asphalt
10cm
20cm
30cm
40cm
Ref
0
0.1
0.2
0.3
0.4
0.5
400 500 600 700 800 900R
efl
ect
an
ce
Wavelength [nm]
Grass1
10cm
20cm
30cm
40cm
Ref
56
Markelin, L., Honkavaara, E., Schläpfer, D., Bovet, S., Korpela, I., 2012.
Assessment of radiometric correction methods for ADS40 imagery. .
Photogrammetrie, Fernerkundung, Geoinformation (PFG) 4/2012
Reflectance RMSEs on bright
tarps • Xpro
• Sensor: Lab calibration
• Atmospheric parameters from images
• ATCOR-4
• Sensor: 1) Lab calibration, 2) Self-calibration (reference targets in image block), 3) In-situ calibration: using single flight line in 1 km to calibrate sensor
• Atmospheric parameters from images
• Best results with Xpro and ATCOR-4 with self calibration
• RMSE on level of 5% at best
• Accuracy influenced by flying height, atmosphere, channel, magnitude of reflectance.
57
Results of Xpro Correction
• plaa
58
At-sensor radiance At-sensor radiance + BRDF
Atmospheric correction Atmospheric correction
+ BRDF
Downey. M., Uebbing, R., Gehrke, S., Beisl, U., 2010. Radiometric processing of ADS
imagery: Using atmospheric and BRDF corrections in production.ASPRS 2010 Annual
Conference, San Diego, California. April 26-30, 2010.
59
Results of Pepita of IGN
• The solar elevation, exposure time and aperture values were well
corrected.
• The BRDF model worked well especially on forest and agricultural parcels.
• Potential problems
• BRDF model did not fit on the lake, high residuals
• In urban areas: the radiometric model may not be convergent
Chandelier, L., Martinoty, G, 2009. Radiometric aerial triangulation for the
equalization of digital aerial images and orthoimages. Photogrammetric
Engineering & Remote Sensing 2009, 75, 193-200.
60
Example 1. Tree species
classification using reflectance
calibrated photogrammetric images • Based on
• Heikkinen, V., Korpela, I., Tokola, T., Honkavaara, E., Parkkinen, J., 2011. An SVM classification of tree species
radiometric signatures based on the Leica ADS40 sensor. IEEE Transac¬tions on Geoscience and Remote
Sensing 49(11), 4539 – 4551.
• Korpela, I., Heikkinen, V., Honkavaara, E., Rohrbach, F., Tokola, T., 2011. Variation and directional anisotropy of
reflectance at the crown scale - Implications for tree species classification in digital aerial images. Remote
Sensing of Environment 115 (8), 2062-2074.
61
Research questions
• Development of methods for tree species classification
using high resolution photogrammetric images
• Reflectance characteristics of tree species: Scots pine,
Norway spruce and birch
• Influence of image data processing level on classification
results: at-sensor radiances, directional reflectance (BRF),
nadir normalized reflectance (BRDF corrected)
• Quadratic discriminant analysis and support vector
machine classification
• Generalization properties of reflectance factors
concerning the need for in-situ training data
62
Determination of visibility and
shading conditions • Estimation of crown
envelope with dense
lidar data: 121 points
per cown
• Illumination condition
for each point by ray
tracing:
• Occlusion: Cloud, tree
itself, adjacent tree
• Shadow: Cloud, self-
shaded, neighbour
shaded, neighbour and
self-shaded
63
© Ilkka Korpela 2011
Illumination in forest
64
© Ilkka Korpela 2011
Reflectance characteristic of tree species
• Plottings as the function of FOV-angle against to the flying direction, approximately in solar principal plane
• Near the solar principal plane, pine, spruce and birch showed anisotropy of ± 30%, with differences between illumination classes, bands and species.
• The within-species variation was high; the coefficients of variation were
13-31% when normalized to the reflectance at nadir.
65
At-sensor radiance Atmospheric
compensation
Atmospheric
compensation,
BRDF correction
Classification results
• Classification experiments with reflectance calibrated data
provided an accuracy of about 75-79% with single ADS40
view and 78-82% with two ADS40 views (Heikkinen et al.,
2011).
• In some cases (the training and classification data from
different flight lines) the use of reflectance calibrated data
provided better classification results than the at-sensor
radiance data
• Empirical BRDF correction did not improve classification
results
66
Example 2. NDVI time series
over the Catalonia by ICC
© Roman Arbiol, Lucas Martínez
67
Process
68
LR4 Images from DMC (without post-process)
Absolute radiometric calibration (by manufacturer or by ICC)
Photogrammetric direct block orientation
Public free WMS service (http access also with OrtoXpres)
(http://www.ortoxpres.cat)
• OrtoXpres fast publication after the flight (a few weeks)
© Roman Arbiol, Lucas Martínez
69
© Roman Arbiol, Lucas Martínez
70
© Roman Arbiol, Lucas Martínez
NDVI WMS layer
71
NDVI WMS layer
1st NDVI layer in 2011. Catalonia (32,000km2 & 25cm GSD)
8bpp (DN from 0 to 255)
Orto-rectified “on the fly” of single photographs without stiching
(all zoom levels are not available)
Service available for most of the SIG environtments : ArcView,
Miramon, gvSIG, etc.
NDVI layer users
Agriculture Department of the Generalitat de Catalunya (Catalonia
regional government) will use NDVI layer it to verify agriculture
policy (ie. wineyard, cereal, etc.)
After, the vegetation layer will be freely disseminated according to
ICC data policy
© Roman Arbiol, Lucas Martínez
Example 3. Influence of solar
elevation in photogrammetric
processes and digital image matching
• Honkavaara, E., Markelin, L., Rosnell, T., Nurminen, K, 2012.. Influence of
solar elevation in radiometric and geometric performance of multispectral
photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing.
67, 13-26.
72
Flight strips in Hyytiälä test site
• Image collection using flying heights 2, 3 and 4 km, GSDs 20, 30, 40 cm, by National Land Survey of Finland DMC
• Data processing in Bae systems Socet Set 5.5 environment, DSMs by NGATE
• Solar elevations: Morning: 25-28º, Noon: 44-48º
73
General evaluation: Strip histograms
• Exposure and aperture were tuned to provide desired histograms
• Strip histograms were not dependent on solar elevation
• Histograms were similar in morning and afternoon
Morning
Noon
2 km 4 km
74
DSM extraction in deep shadows (1)
Height
artefacts in
shadows
75
DSM extraction in deep shadows (2)
• Height variation 2-3 times higher in
shadows
Noon Morning
0
0.1
0.2
0.3
0.4
0.5
0.6
20 30 40 20 30 40 20 30 40 20 30 40
morning noon morning noon
shadow field sunny field
av_sdev
76
Results
• Histograms similar in morning and noon
• Point cloud point density
• Typically the point interval of GSD successful
• Detailed analysis showed that in sun illuminated areas point
cloud quality similar
• Potential problems in shadowed areas
• In planar targets increased height variation
• Failures of measuring road surfaces in deep shadows
• Reflectance calibration difficult
• Low dynamic range
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Example 4. Radiometric processing in
hyperspectral UAV remote sensing
78
UAV operation
ISPRS Hannover Workshop 2013, 21 – 24 May 2013,
Hannover, Germany 79
UAV
•Autopilot
•IMU
•GPS
Payload
•Spectral imager
•High spatial
resolution imager
•GPS
•Irradiance sensors
Ground control station
•Mission design and control
•Insitu reference measurements: irradiance,
reflectance targets,
•In typical flight 100-500
data cubes with 20-40
spectral layers
•Georeferencing data
•Irradiance data
•Insitu data
Fabry-Perot interferometer based
tuneable spectral camera • Hyperspectral imagery in frame
format: stereoscopic, spectrometric data
• Weigh 600 g
• Spectral data cube by changing the width of Fabry-Perot air gap
• Developed by VTT Technical Research Finland (Heikki Saari)
• Custom optics: C=10.9 mm, F-number < 3.0
• CMOS detector: 1024 x 648 pixels, Pixel 11 μm
• Application based filter selection between 400-1000 nm
• 500-900, 600-1000, 400-500, … nm
• Spectral resolution 10-40 nm @ FWHM
• Commercial systems by Rikola Oy
ISPRS Hannover Workshop 2013, 21 – 24 May
2013, Hannover, Germany 80
Vihti campaigns
81
• MTT agricultural test area in Vihti
• Hyperspectral UAV camera
• July 2, 2012 10:39 and 10:50 local time (UTC +3).
• Poor illumination conditions with fluctuating levels of cloudiness
• Flying altitude 140 m -> GSD of 14 cm
• Irradiance measurements by UAV and in ground
• Radiometric block adjustment
0
0.5
1
1.5
2
2.5
3
Image
ground, 29
uav
BA: relA, 29
average, 29
Correction factors to reference image
Image mosaics and sample spectra, FGI
82
No corr
UAV
Ground
Image
based
Coefficient of variation
• Average
coefficient of
variation in tie
points
• Without correction
0.14-0.18
• UAV: 0.1-0.12
• Ground: 0.06-0.09
• Block adjustment:
0.04-0.07
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0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
500 600 700 800 900
Wavelength (nm)
no corr
uav
ground
BA: relA
Coefficient of variation
Biomass estimation by knn-estimator
84
No correction
R2: 0.64
NRMSE: 24.9%
Ground irradiance
R2: 0.74
NRMSE: 17.8%
Block adjustment
R2: 0.74
NRMSE: 16.8%
Concusions
85
Data processing • Accurate and efficient radiometric processing methods are now available
• Accurate calibration methods required • Laboratory • Test field
• Reflectance images by utilizing sensor calibration, atmospheric models and BRDF correction
• With 1 st generation methods, 5% accuracy was demonstrated
• Operational: no ground control needed, fully automated
• Solutions for all major photogrammetric cameras: ADS, DMC, UltraCam
• Improvements still needed to the methods • Shadowed areas
• Topographic correction
• Class specific BRDF correction
• Absolute reflectance
• Temporal differences
• NMAs are implementing next generation radiometric correction methods in their processing lines
• Swisstopo: Swissimage standard, Remote sensing basic
• IGN France: Pepita
• ICC
• …
86
Outlook • Mutlispectral photogrammetric imagery are suitable for
remote sensing use.
• Lots of unused potential.
• What is the most cost efficient approach for large area
accurate topographic mapping, forest information
derivation, long time GIS data maintenance, etc.?
• Reflectance calibrated multiple directional observations
• BRDF corrected normalized reflectance
• Campaign specific classifications without controlled radiometry
• Single sensor vs multiple sensor applications
• Feasiblity of 3D reconstruction and ray tracing in object
interpretation?
• Many advantages are expected from rigorous processing.
87
Thank you!
Questions?
88