Ocean Colour Climate Change Initiative5... · →train a machine to mimic a human‘seye/brain for...
Transcript of Ocean Colour Climate Change Initiative5... · →train a machine to mimic a human‘seye/brain for...
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Ocean Colour Climate Change Initiative
AI in Ocean ColourCarsten Brockmann (BC), Thomas Jackson (PML)
Material by M. Paperin, J. Wevers, K. Stelzer, D. Müller & Roland Doerffer (BC, HZG)
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Slide 2
Ocean Colour Problem
• Radiative transfer – highly non linear process
▪ Not uniquely reversible
• Additional problems
▪ (S)IOPs highly variable
– space, time
▪ parametrisation of
radiative transfer equation
– inherent optical properties
of atmosphere and water
▪ Clouds
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Slide 3
Cloud Screening using Machine Learning
• Idea:
▪ our eye and brain is the best cloud detector
▪ → train a machine to mimic a human‘s eye/brain for cloud detection
▪ Eumetsat IAVISA Study, 2008
• Implementation
▪ Collection of manually labelled pixels = training dataset
– No algorithm or any other machine involved in the process of identification
and labelling of a pixel
▪ Training of a neural network
– Classical fully connected multi-layer perceptron
– Feedforward – backpropagation training
– (SNNS toolkit, German award for educational software 1991)
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Slide 4
Training Dataset
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Slide 5
Training Dataset
• A priori definition of classes and frequency
distribution
• Hierarchy of classes
MERIS: 110 000 pixels
VIIRS: 60 000 pixels
OLCI: 44 100 pixels
53000
30654
12395
17509
750
22306
11522
5422
4042
1320
4987
2751
1265
971
0 10000 20000 30000 40000 50000 60000
Total number of pixels
Cloudy
Totally Cloudy
Semi-transparent clouds
Other turbid atmosphere
Clear
Clear sky land
Clear sky water
Clear sky snow/ice
Other clear cases
Other
Floating ice
Glint
Cloud shadow
distribution of surface types (PB-V)
PB-V: 53 000 pixels
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Slide 6
NN Performance
opaque cloud
clear Land
semi-transparent cloud
spatially mixed cloudclear water
clear snow/ice
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Slide 7
Validation
1 = Opaque
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Slide 8
Validation
1 = Opaque
2 = Semi-transparent cloud
3 = Thick semi-transparent cloud
4 = Average density
semi-transparent cloud
5 = Thin semi-transparent cloud
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Slide 9
Validation
1 = Opaque
2 = Semi-transparent cloud
3 = Thick semi-transparent cloud
4 = Average density
semi-transparent cloud
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Slide 10
Example OLCI, 2016428
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Slide 11
Inversion of the radiative transferCoupled ocean – atmosphere system
• Idea:
▪ Radiative transfer physics are well understood
▪ Formulation of „forward“ problem possible
▪ Numerical RT models well advanced and validated
→ Calculate a comprehensive database of spectra for representative waterand atmosphere conditions
→ Inversion by machine learning
• Implementation:
▪ Decomposition of problem into 2 parts (otherwise the manifold of thesolution space would be too large): ocean and atmosphere
▪ Set of neural nets for the inversions
▪ Starting with SNNS in mid-1990‘s for MERIS
– MLP with ffbp training
▪ Switching to Tensorflow/KERAS in 2018
– Experimenting with different architectures
– Same quality can be achieved with much less training samples
– Speed of the training significantly improved
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NEURAL NETWORK BASED PROCESSING
water bio-opticalmodel
atmosp. parametrisation
aerosol
SIOPs
RT atm.
RT ocean
RT simulations: MERIS, OLCI,
MODIS, VIIRS, SeaWiFS,
S2 MSI, L8 OLI, RE
NNs training
FeedforwardBackpropagation MLP
aaNN
IOP
fwd
kd
rw
unc
SNAP C2RCC S2 Processor
SNAP C2RCC S3 Processor
ProcessorOLCI GS
SNAP C2RCC S2 Processor
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TRAINING DATASETS:ATMOSPHERE MODEL
Solar zenith angle: 0-75 deg
Surface pressure: 800 – 1040 hPa
Max. rho_toa at 865 nm limited to 0.8
AOD Angstrom coeff.
AOD
frequency
frequency
Angstr.
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TRAINING DATASET: BIO-OPTICAL MODEL
ranges derived from in-situ measurements
frequency
frequency
frequency
frequency
frequency
ad agapig
bp bw
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TRAINING DATASET: BIO-OPTICAL MODEL
btot ad
apig
ag
Co-variances derived from in-situ measurements
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NEURAL NETWORK BASED PROCESSING
water bio-opticalmodel
atmosp. parametrisation
aerosol
SIOPs
RT atm.
RT ocean
RT simulations: MERIS, OLCI,
MODIS, VIIRS, SeaWiFS,
S2 MSI, L8 OLI, RE
NNs training
FeedforwardBackpropagation MLP
aaNN
IOP
fwd
kd
rw
unc
SNAP C2RCC S2 Processor
SNAP C2RCC S3 Processor
ProcessorOLCI GS
SNAP C2RCC S2 Processor
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VERIFICATON (SIMULATED DATA)ATMOSPHERE
water leaving reflectance,400 nm water leaving reflectance, 560 nm
„truth“
Re
trie
va
l (N
N)
Re
trie
va
l (N
N)
„truth“
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VERIFICATON (SIMULATED DATA, WATER)
apig
Only water part(NN validation) Atmospere + Water
Adding extreme water cases(masking effect)
„truth“
retr
ieved
by
NN
apig
apig
„truth“
retr
ieved
by
NN
„truth“
retr
ieved
by
NN
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VALIDATION COMPARISON AGAINST IN-SITU
Comparison OLCI S3A rho_w_nn with
AAOT rhon_w_is
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NEURAL NETS FOR CONSISTENCY CHECKS
water → forward net fed
with retrived IOPs
atmosphere →
autoassociatove neural net
TO
A r
efle
cta
nce
wavelength wavelength
wa
ter
lea
vin
gre
fle
cta
nce
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UNCERTAINTIES
RT Database
IOPs
NNIOP
rho_w
IOPs, estimated
∆(IOPs)
traininguncer-tainty
net
NNuncer-tainty
IOPs, estimated
∆(IOPs)
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UNCERTAINTIESa
pig
longitude
apig Uncert. of apig
CH
L c
onc.
longitude
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S3B OLCI 20190104
rho_toarho_w
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Chlorophyll and TSM
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Adg and z90max
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TSM wit al3ex model
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Slide 27
Conclusion
• The construction of the population (training sample, validation sample) ismost critical for the quality of the retrieval quality
▪ Cloud screening: representing all different types of clear sky and cloudyconditions
▪ Covering the range of optical properties of the water body and the atmosphere
▪ Reflecting the inner structure (dependencies, co-variances) of the IOP space
▪ Containing sufficient samples of everything which shall be retrieved
– Constructing the training data set such that it represents the frequency distribution of conditions as they appear in reality is a wrong approach; It would cause rare cases being poorly retrieved.
• The choice among different AI methods (deep learning, RF, conv.NNs, …) has a minor effect.
▪ All tested methods so far deliver excellent performance of inverting the validationdataset.
▪ However, a 99% accuracy on the validation dataset (which is from the same population as the training dataset) is irrelevant if the population is not properlyrepresenting nature.
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Slide 28
Future use – Water Type Classification
Objective: Increased automation of processing up to end of water class set
generation allows more time for scientific interpretation and rapid
updates/application to new data sources.
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Slide 29
Last Slide
• RT inversion in a coupled ocean-atmosphere system is a highly
non-linear, underdetermined problem
▪ „Ocean Colour retrieval seems impossible“ (Roland Doerffer)
• Articifial Intelligence is a method to address this problem
▪ „Let the data tell us the solution“ (Helmut Schiller)