1. SAP4PRISMA
DEVELOPMENT OF ALGORITHMS AND PRODUCTS
FOR SUPPORTING THE ITALIAN HYPERSPECTRAL
PRISMA MISSION: THE SAP4PRISMA PROJECT
Pignatti S., Acito N., Amato U., Casa R., de Bonis R., Diani M.,
Laneve G., Matteoli S., Palombo A., Pascucci S., Romano F., Santini
F., Simoniello T., Ananasso C., Zoffoli S., Corsini G. and Cuomo V.
2012 Munich IGARSS, 22-27 July

2. OUTLINE
• PRISMA mission highlights
• SAP4PRISMA project
• Data processing
• Products
– land degradation and natural vegetation
– crops monitoring
– natural and human-induced hazards
• Conclusions
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

3. PRISMA - context and background
Mission Statement:
“… a pre-operative small Italian hyperspectral mission, aiming to qualify the technology,
contribute to develop applications and provide products to institutional and scientific users for
environmental observation and risk management …”
…
Operational mission +
2008- 14 Future … TBD
System deployment and exploitation
2006-07 System design and development
PRISMA
System architecture & preliminary design
2000-02 User Needs - consolidation
JHM
Critical technologies developments
System architecture & preliminary design
User Needs Hypseo
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Conclusion

4. Mission highlights
Coverage:
World-wide
Specific Area of interest (AoI)
System Capacity:
Acquired data volume:
Orbit: >50.000 km2
Daily >100.000 km2
Daily products generation: 120 HYP/PAN img
System Latencies (inside AoI):
Re-look time: < 7 days
Response time: < 14 days
Mission modes: PRISMA Hyperspectral
Primary: User driven sensor utilizes prisms to
obtain the dispersion of
Secondary: Data driven (background mission) incoming radiation on a 2-D
Life time: matrix detectors
5 years
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Conclusion

5. Key imaging and payload requirements
 Radiometric Quantization: 12 bit
 Swath / FOV: 30 km / 2.45°
 SNR
 Spatial GSD (elementary geom. FoV): PAN: 240:1
 PAN: <5 m (2x6000 pixels) VNIR: 200:1 (400-1000 nm)
 HYP: <30 m (1000x256 pixels) 600:1 (@650nm)
SWIR: 200:1 (1000-1750 nm)
 Spectral ranges:
400:1 (@1550nm)
 PAN camera: 400-700 nm
100:1 (1950-2350 nm)
 HYP instrument (contiguous spectrum) 200:1 (@2100nm)
 VNIR: 400-1010 nm (66 bands)  Absolute radiometric accuracy: <5%
 SWIR: 920-2500 nm (171 bands)  Keystone/Smile > 0.1 GSD/ ± 0,1 SSI
 Spectra Sampling Interv. (SSI): 10 nm
 Spectral resolution: 12 nm FWHM
 Aperture diameter: 210mm
 MTF (@Nyquist frequency)
 PAN > 0.30
 VNIR > 0.30
 SWIR > 0.20
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Conclusion

6. SAP4PRISMA Research and activity plan
Research activities development for the optimal use of hyper-spectral PRISMA data:
the SAP4PRISMA project
• Data quality assessment and enhancement
• Development of classification algorithms
• Development of L3/L4 products using hyperspectral information for:
soil quality, soil degradation and natural vegetation monitoring
crop monitoring and agriculture applications
natural and human-induced hazards
prototipal
products test &
Set Up products
development validation
development
2011 2014
Many synergies could be envisaged with the activities faced by the other
hyperspectral missions (i.e. EnMAP, HysPiri and HISUI)
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

7. SAP 4 PRISMA development of algorithms and products for applications in
agriculture and environmental monitoring to support the PRISMA mission
SAP4PRISMA
WP1 WP2 WP4
WP3
Data set individuation and Innovative WP5
Pre-processing and
Manag. CAL/VAL strategies methodology of Applicative products
data quality
classification
WP1-A WP2-A WP3-A WP4-A WP5-A
PRISMA like data noise and data Hard classification land degradation and
research activities selection dimensionality methods vegetation monitoring
reduction
WP2-B WP3-B WP4-B WP5-B
WP1-B
Definition of the CAL/ cloud identification Soft classification Application for
scientific support
VAL strategies and classification methods, unmixing agriculture
to ASI
WP3-C WP5-C
atmospheric Natural and man
correction induced
environmental risks
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Conclusion

8. SAP4PRISMA Research and activity plan
The research is carried out in synergy between the WPs according to this scheme
WP3
Data quality
Data dimensionality WP4
Classificators
Hard & Soft
WP2
CAL/VAL
WP5
Products development
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

9. WP2 - “PRISMA-like” synthetic data generation
Criteria for “PRISMA-like” synthetic data generation have been outlined on the basis
of the data sets available to the team to support mission requirements consolidation
 Spectral reflectance signatures acquired by a spectroradiometer (such
as USGS spectral library);
 Radiance images acquired by sensors characterized by both higher
spectral and spatial resolutions (such as HySpex sensor);
 Radiance images acquired by “PRISMA-like” sensors, i.e.
characterized by spectral and spatial resolutions similar to those of
PRISMA (e.g., Hyperion sensor);
 Simulated PRISMA Images and “HYP and PAN fused images” by
other dedicated groups
For each category of data, suitable methodologies for “PRISMA-like” synthetic data
generation have been defined
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

10. WP 2 - “PRISMA-like” synthetic data generation
ρ( λ)
Spectral reflectance signatures acquired by a
DATA BASE
spectroradiometer
λ
Linear Mixing Model generation Endmember extraction and
Spectral sampling (Statistical hypotheses over unmixing (“soft” classification)
abundances)
PDF mixture model generation Clustering
Spectral sampling (parametric statistical models) (“hard” classification)
Spectral features extraction Specific indexes computation
Spectral sampling (e.g. absorption) (e.g. NDVI)
Hyperspectral image acquired by sensors characterized by both spectral and
spatial high-resolutions
Spectral resolution
degradation Spatial resolution degradation “PRISMA-like” image
PRISMA SRF PRISMA PSF
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Conclusion

11. WP3 - Pre-processing and data quality
• Methodologies for reducing dimension and noise of
data
Hyperion test site Sicily
On radiance and reflectance images and/or a limited
number of “superchannels”
Selection of endmembers in images and estimation of
abundancy in pixels will be the target application
• Algorithms for identifying and classifying clouds
Physically based: relying on Radiative Transfer models
Statistically based: involving discriminant analysis and
linear transforms; mixed statistical/physical algorithms
G. Algorithms for the atmospheric correction
Taking into account of adjacency effects, view angle and
landscape elevation dependences. MODTRAN and 6S Cloud mask
(ML algorithm)
based August 31, 2011
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Conclusion

12. WP4 - Innovative methods for classification
WP4A – Hard classification
Clustering based on Gaussian mixture model:
 Mixture parameters estimation via Expectation Maximization (EM)
 Pixel assignment criterion : Minimum Mahalanobis distance
Unsupervised initialization for the EM algorithm
Automatic selection of the clusters number
Experiments on simil-PRISMA data
WP4B – Soft classification
 Endmembers extraction algorithms.
 Estimation of the endmembers number by means of the NWHFC algorithm
 Experiments on simil-PRISMA data
WP4A & WP4B – simil-PRISMA data: HYPERION images
 Pre-processing: fixed pattern noise reduction
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

13. WP4A-unsupervised clustering via EM (1/2)
Unsupervised initialization based on the parameters estimates obtained on randomly
selected training sets
μ11) , μ (21) ,..., μ (N)C , 
( 1
NC  ( 1) ( 1)
 
( 1) 
Γ1 , Γ 2 ,..., Γ N C , 1
 ( 1) ( 1) ( 1)  μ1k *) , μ (2k *) ,..., μ (N C ) , 
( k*
Randomly π 1 , π 2 ,..., π NC 
   ( k *) ( k * )
 
selected training training set - 1 ( k *) 
Clustering via Γ1 , Γ 2 ,..., Γ N C ,
set  ( k *) ( k *)
EM ( k *) 
Best result selection: π 1 , π 2 ,..., π N C 
 
Log-likelihood
maximization
Randomly Clustering via
selected training EM
training set - K
set μ1K ) , μ (2K ) ,..., μ (NKC) , 
(

 (K) (K) (K) 

Γ1 , Γ 2 ,..., Γ N C ,
 (K) (K) (K) 
π 1 , π 2 ,..., π NC 
 
1
k * = arg max log Λ( N) ( X )
k
k =1,..., K
{ c
} Selection
criterion
[ ( )]
Np
Log-likelihood
function log Λ N ( X ) = ∑ log p x i ; μ1k ) , μ (2k ) ,..., μ (Nk ) , Γ1k ) , Γ (2k ) ,..., Γ (Nk ) , π 1( k ) , π 2k ) ,..., π Nk )
(k)
c
( (
C C
( (
C
i =1
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

14. WP4A-unsupervised clustering via EM (2/2)
Automatic selection of the number of the clusters Nc: log-likelihood function based
criterion
μ11) , μ (21) ,..., μ (N)C , 
( 1
N C1)
(  ( 1) ( 1) 
Clustering via EM 
Γ1 , Γ 2 ,..., Γ N C ,
( 1)  τ
with random
 ( 1) ( 1) ( 1)  log Λ1 ( X )
initialization π 1 , π 2 ,..., π NC  Log-likelihood
  2
(optimized) function
computation Best result
selection:
Log-likelihood
relative variation
Clustering via EM criterion
with random Log-likelihood
initialization function
N CM )
(
(optimized) μ1M ) , μ (2M ) ,..., μ (NC ) , 
( M computation log Λ M ( X )
 (M ) (M )  μ1N C ) , μ (2N C ) ,..., μ (N C ) , 
( * * *
N
 (M ) 
Γ1 , Γ 2 ,..., Γ N C ,  * C

 ( N C ) ( NC ) *
( N C ) ,
*
 (M ) (M ) (M )   Γ1 , Γ 2 ,..., Γ NC 
π 1 , π 2 ,..., π NC 
   ( NC ) ( N C )
* *
(N* ) 
π 1 , π 2 ,..., π N CC 
 
N c* = min I * 2
{ [ ] }
I ≡ n : n ∈ N c ,..., N cM ∩ Ω , I * ≡ { n : I ∩ Ω}
1
log Λ n +1 ( X ) − log Λ n ( X )
Ω ≡ { n : ρn < τ }, ρn = ×100
log Λ n ( X )
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

15. WP4B-Soft classification
Noise variance Λ Noise Endmembers
estimation whitening estimation algorithm
X XW
Covariance Correlation
matrix matrix
Noise Whitened HFC
estimation estimation
ˆ
CX ˆ
RX
NWHFC
Eigenvalues Eigenvalues
extraction extraction PPI VCA AMEE
{λ }
C L
l l =1 {λ }
R L
l l =1 N-FINDR
pf Neyman-
Searching for the
{ ei } i =1
ˆ
Ne
Pearson
based simplex with
detector ˆ
Ne maximum volume
HFC
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

16. WP4 – First test
Sensor HYPERION (EO-1)
Geographic area South Sicily
Acquisition date 22-07-2001
Product L1R (no geometric correction)
Spatial resolution 30m
Sub-image
Spectral resolution 10 nm 200x200 pixels (~6Km x
N. of channels 175 6Km)
Unsupervised endmembers
Unsupervised clustering
extraction (WP4B)
(WP4A)
NWHFC with Pf = 10
−5
15 x 10
6
N e = 33
7
6
5
radianza spettrale
10
4
3
2
1
5 0
400 600 800 1000 1200 1400 1600
wavelength (nm)
1800 2000 2200 2400
Endmembers spectra
N C = N C = 19
*
Endmembers positions
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WP5 activities 2012 Munich IGARSS, 22-27 July
Conclusion

17. WP5: Applicative products
The overall objective of this WP is the development
of PRISMA data applications that are feasible, useful and innovative to meet
the needs of end users interested in agriculture, land degradation and
the management of natural and human-induced hazards
• WP5_A - Development and improvement of
methodologies for land degradation and natural
vegetation monitoring
• WP5_B - Development and improvement of
methodologies and algorithms for agricultural areas
• WP5_C - Applications for the management of natural and
human-induced hazards
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Conclusion

18. WP5A: Land degradation and natural vegetation monitoring
Rock outcrop Classification of
GSD 1.5 m 22.500 m 2
Shrubs (3222) natural areas up to the
Arid grassland (3211) 4th Corine level for
Beech forest (3115) MIVIS and Hyperion
VHR 150 m.
(subpixel) on the
GSD: 7 m 484 pixels Pollino National Park
UNMIXING ACCURACY
HYP high spatial resolution
Prisma-like data RE
GSD: 30 m 25 pixels %=5.03
Hyperion
Classific Endmember diff.
ation Shrubs 3.2%
PRISMA like
Beech 1.56%
Grassl. 1.67%
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Conclusion

19. WP5A: Ecosystem analysis and vegetation health status
Accurate natural vegetation monitoring procedures including multi-temporal and multi-sensor data
to understand its distribution useful in the landscape metrics analysis (block level classification)
p ij
SHAPE =
minp ij
pij is the perimeter of patch ij
min pij min is the minimum
perimeter possible pij for a
figure having the area of the 2ln( 0.25 pij )
patch ij FRACT =
aij is the area of the patch ij
lnaij
Measures the joint edges of the patch and is Measures the complexity of the shape of the patch
connected with the level of naturalness of the cover: over a range of spatial scales assessing at the
- High natural: edges articulated same time the configuration of the perimeter and
- Low natural: smooth edges the size of the block considered.
High levels of FRACT, for very small plugs, may
The influence of human activities increase the
give an indication of fragmentation processes in
regularity of edges (e.g. forest near cultivated land)
place
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Conclusion

20. WP5A: Land degradation and natural vegetation monitoring
Example of saltwater intrusion
Data integration:
FRACT index concerns the • Satellite (including
patch regularity Hyperspectral) based
Negative trends i.e. an landscape metrics
increase of the shape Fract and Coastal • Geophysical surveys
regularity indicates for a
variations • Chemical-physical
measurements
decrease of naturality
Positive trends provides an 1987- 2004
indication of ongoing 15B
fragmentation processes 17B
2B
11B
14A
Forested area Salt contamination limit
Dune
shore
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Conclusion

21. WP5A: Land degradation: soil quality and soil degradation – ongoing
activities (organic matter, CaCO3, iron content, salinity, etc.)
Lab experiments for soil Mixing Soil – NPV
Mixing Soil – PV
Spectral Index vs
texture analysis GSI ± 1σ
unmixing for soil erosion
silt very fine sand
GSI
fine sand
Soil percentage
Grain size (micron)
Mean value = 0.361
St. Dev. = 0.107
9/7/2007 26/6/2012
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Conclusion

22. WP5B: Scientific and application tasks for agriculture:
Development and improvement of algorithms and methods for estimating from HYS data
Soil properties
Biophysical and biochemical
variables of agricultural
crops
Variables of agronomic and
environmental interest,
through the assimilation of
remote sensing data into
working models
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Conclusion

23. WP5B - Scientific and application tasks for agriculture
Soil components at field scale: preliminary results
Samples were collected in two fields from the 0-30 cm layer by Maccarese, Central Italy
means of a gouge auger
Dataset Variable Mean ± st.dev Min Max Skewness
clay 38.9 ± 9.2 15.3 56.1 -0.18
B071
132 silt 19.2 ± 3.7 8.4 28.9 0.36
samples
sand 41.9 ± 10.9 15.0 62.0 -0.12
Soil sample collection
Airborne CHRIS RMSE: root mean squared error R
MIVIS Lab analysis (clay, silt, sand)
RPD: ratio of performance to deviation
RPD>2 accurate models
RPD between 1.4 and 2 intermediate
RPD<1.4 no predictive ability
Remote sensing data acquisitions: Chang and Laird (2002)
MIVIS & CHRIS
Soil point Kriging
measurements values
Calibration PLSR models
(B071B or random) Validation B071A
field or random
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Conclusion

24. WP5B - Scientific and application tasks for agriculture
Soil components at field scale: preliminary results
Block kriging CHRIS-PROBA MIVIS
CHRIS – B071B x B071A CHRIS – random
Calibration: 468
Validation: 390
MIVIS – B071B x B071A MIVIS – random
Calibration: 6435
Validation: 4771
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Conclusion

25. WP5B - Scientific and application tasks for agriculture
Crop components: preliminary results
1 July CHRIS
26 July CHRIS
LAI Biomass LAI Biomass
Testing of non-linear data modeling techniques like PLSR models for the
assessment of LAI and Biomass by using as validation on situ data campaigns on
maiz crop fields.
Development of methods and algorithms for the estimation of variables of
agronomic and environmental interest through the assimilation of hyperspectral
remote sensing data into working models (limited to cereal crops)
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Conclusion

26. WP5C: Applications for the management of natural and human-induced hazards
Identification, monitoring and possible Airborne Hyp
image:
quantification of pollutants through Red Dust
specific spectral features relatable to dispersion map
as attained by
changes in chemical composition of applying SFF
the polluted soil algorithm.
Yellow depicts
low-medium
Analysis and optimization of methods RD surface
and algorithms for the estimation of concentration,
red represents
soil/water pollution due to human high RD
activities and natural hazards surface
concentration.
according to the PRISMA sensors’
characteristics
λ 680 − λ 549
RDI =
λ 680 + λ 549
Distribution maps of pollutants
Validation/Calibration of the methodologies and
products and Detection Limit assessment of main
pollutants spectral absorptions features on the
PRISMA spectral sampling and noise characteristics
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2012 Munich IGARSS, 22-27 July SAP4PRISMA
Conclusion

27. WP5C: Applications for the management of natural
and human-induced hazards - Damage severity index (post fire)
Build an index able to estimate the
severity of the damage in burned
areas.
The work will be developed in three
main phases:
1. Simulation of reflectance spectra by
radiative transfer models, at foliar
level and vegetation structure level
divided in layers like shown in
figure;
3. Construction of the index based on
the results obtained by simulations
and calibration based on real image
data.
Burn Severity Scale
No damage Low Moderate High
5. Development of an algorithm for the
0 0.5 1 1.5 2.0 2.5 3.0
automatic calculation of the index
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2012 Munich IGARSS, 22-27 July SAP4PRISMA
Conclusion

28. Conclusions and Future work
PRISMA mission will provide major increase of systematic
HYP acquisition capacities with significant spectral
performances so enabling a major qualitative/quantitative step
in services provided
The SAP4PRISMA project within the 3 years of remaining
activity will be focused on both technical issues, related to
the mission itself, and the development of Level3/4
PRISMA products
SAP4PRISMA aims to demonstrate that improved service
performances are achievable by applying innovative
hyperspectral remote sensing methods for:
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Conclusion

29. Conclusions and Future work
• Soil erosion assessment and monitoring of Land degradation processess
and extraction of topsoil properties under varying surface conditions,
considering spatio-temporal variations in moisture and vegetation cover
• Analysis of PRISMA retrievable information for Crop monitoring and
biophysical and biochemical variables of agricultural crops; improved
discrimination of crop stress caused by nitrogen deficiency, crop disease
and water stress
• Retrieving of variables of agronomic and environmental interest, through
the assimilation of hyperspectral remote sensing information into crop
working models (e.g., crop production and nitrogen content)
• Disaster mapping: identification and quantification of surface pollutants
mapping
through their specific spectral signatures or specific features (changes in
chemical composition of polluted soils); damage severity index (post fire)
development
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Conclusion

30. Conclusions and Future work
• Project results are expected to substantiate the needs for new
observation techniques to be implemented in the next generation of
observation satellites (PRISMA as a precursor)
• The PRISMA impact will be demonstrated through pilot tests and
exercises, based both on simulation data and on real events, when
possible and appropriate
Synergy with other EU hyperspectral programs and their scientific
related projects can be a crucial point for the next EU HYP missions!!
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Conclusion