DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTRAL PRISMA MISSION: THE...

DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTRAL

PRISMA MISSION: THE SAP4PRISMA PROJECT

Pignatti S.,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.

SAP4PRISMA

2012 Munich IGARSS, 22-27 July

OUTLINE

• PRISMA mission highlights

• SAP4PRISMA project

• Data processing

• Products

– land degradation and natural vegetation

– crops monitoring

– natural and human-induced hazards

• Conclusions

PRISMA missionSAP4PRISMA prjWP5 activitiesConclusion


User Needs

Critical technologies developments

2000-02

HypseoSystem architecture & preliminary design

System deployment and exploitation

System design and development

2008- 14

PRISMA

PRISMA - context and background

2006-07

System architecture & preliminary design

JHMUser Needs - consolidation

Future …

…

Operational mission+

TBD



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 …”

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:

Primary: User driven

Secondary: Data driven (background mission)

Life time:

5 yearsPRISMA missionSAP4PRISMA prjWP5 activitiesConclusion


PRISMA Hyperspectral sensor utilizes prisms to obtain the dispersion of incoming radiation on a 2-D matrix detectors

Key imaging and payload requirements

Swath / FOV: 30 km / 2.45°

Spatial GSD (elementary geom. FoV): PAN: <5 m (2x6000 pixels) HYP: <30 m (1000x256 pixels)

Spectral ranges: PAN camera: 400-700 nm HYP instrument (contiguous spectrum)

VNIR: 400-1010 nm (66 bands)

SWIR: 920-2500 nm (171 bands)

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

Radiometric Quantization: 12 bit SNR

PAN: 240:1VNIR: 200:1 (400-1000 nm)

600:1 (@650nm)SWIR: 200:1 (1000-1750 nm) 400:1 (@1550nm)

100:1 (1950-2350 nm) 200:1 (@2100nm)

Absolute radiometric accuracy: <5% Keystone/Smile > 0.1 GSD/ ± 0,1 SSI



6

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

Many synergies could be envisaged with the activities faced by the other hyperspectral missions (i.e. EnMAP, HysPiri and HISUI)

Set Upprototipal products

development

productsdevelopment

test &validation

2011 2014



SAP4PRISMA

WP1

Manag.

WP1

Manag.

WP4Innovative

methodology of classification

WP4Innovative

methodology of classification

WP5 Applicative products

WP5 Applicative products

WP3Pre-processing and

data quality

WP3Pre-processing and

data quality

WP2Data set individuation and

CAL/VAL strategies

WP2Data set individuation and

CAL/VAL strategies

WP2-APRISMA like data

selection

WP2-APRISMA like data

selection

WP2-BDefinition of the

CAL/VAL strategies

WP2-BDefinition of the

CAL/VAL strategies

WP3-Anoise and data dimensionality

reduction

WP3-Anoise and data dimensionality

reduction

WP3-Bcloud identification and classification

WP3-Bcloud identification and classification

WP3-Catmospheric correction

WP3-Catmospheric correction

WP5-Aland degradation and vegetation monitoring

WP5-Aland degradation and vegetation monitoring

WP5-BApplication for

agriculture

WP5-BApplication for

agriculture

WP5-CNatural and man

induced environmental risks

WP5-CNatural and man

induced environmental risks

WP4-AHard classification

methods

WP4-AHard classification

methods

WP4-BSoft classification

methods, unmixing

WP4-BSoft classification

methods, unmixing

WP1-A

research activities

WP1-A

research activities

WP1-Bscientific support

to ASI

WP1-Bscientific support

to ASI

SAP4PRISMASAP4PRISMA

SAP 4 PRISMA development of algorithms and products for applications in agriculture and environmental monitoring to support the PRISMA mission



8

Research and activity plan

The research is carried out in synergy between the WPs according to this scheme

WP3Data quality

Data dimensionality WP4ClassificatorsHard & Soft

WP5Products development

WP2CAL/VAL

SAP4PRISMA



9

WP2 - “PRISMA-like” synthetic data generation

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

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

For each category of data, suitable methodologies for “PRISMA-like” synthetic data generation have been defined



10

WP 2 - “PRISMA-like” synthetic data generation

DATA BASE

Spectral reflectance signatures acquired by a spectroradiometer

Linear Mixing Model generation (Statistical hypotheses over

abundances)

Endmember extraction and unmixing (“soft” classification)Spectral sampling

PDF mixture model generation (parametric statistical models)

Clustering (“hard” classification)Spectral sampling

Spectral features extraction(e.g. absorption)

Specific indexes computation (e.g. NDVI)Spectral sampling

Hyperspectral image acquired by sensors characterized by both spectral and spatial high-resolutions

Spatial resolution degradationSpectral resolution

degradation

PRISMA SRF PRISMA PSF

“PRISMA-like” image



A. Methodologies for reducing dimension and noise of data

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

A. 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

C. Algorithms for the atmospheric correction

Taking into account of adjacency effects, view angle and landscape elevation dependences. MODTRAN and 6S based

WP3 - Pre-processing and data quality



Cloud mask(ML algorithm)August 31, 2011

Hyperion test site Sicily

12

WP4 - Innovative methods for classification

WP4A – Hard classification

WP4B – Soft classification

Unsupervised initialization for the EM algorithmAutomatic selection of the clusters numberExperiments on simil-PRISMA data

Endmembers extraction algorithms. Estimation of the endmembers number by means of the NWHFC algorithm Experiments on simil-PRISMA data

Clustering based on Gaussian mixture model: Mixture parameters estimation via Expectation Maximization (EM) Pixel assignment criterion : Minimum Mahalanobis distance

WP4A & WP4B – simil-PRISMA data: HYPERION images

Pre-processing: fixed pattern noise reduction



13

WP4A-unsupervised clustering via EM (1/2)

Randomly selected training

set

training set - 1

training set - K

Clustering via EM

Clustering via EM

112

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Best result selection:Log-likelihood maximization

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Unsupervised initialization based on the parameters estimates obtained on randomly selected training sets

X k kN

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,...,1

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CCCc

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i

kN

kkkN

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1212121 ,...,,,,...,,,,...,,;loglog ΓΓΓμμμx

Log-likelihood function

Selection criterion

1

Randomly selected training

set

1



14

WP4A-unsupervised clustering via EM (2/2)

Automatic selection of the number of the clusters Nc: log-likelihood function based criterion

Clustering via EM with random initialization (optimized)

1 CN

MCN

112

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Best result selection:

Log-likelihood relative variation

criterion

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computation

log 1 X

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log

loglog ,:

: * ,,...,:

*min

1

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InINNnnI

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Clustering via EM with random initialization (optimized)


computation



15

WP4B-Soft classification

Endmembers estimation algorithm

Noise variance estimation

Noise whitening

X

WX

Correlation matrix

estimation

Covariance matrix

estimation

Eigenvalues extraction

Eigenvalues extraction

Neyman-Pearson based

detector

XRXC

L

lCl 1 L

lRl 1

eNHFC

No

ise

Wh

iten

ed H

FC

NW

HF

C

fp Searching for the simplex with

maximum volume

eN

ii

ˆ

1eN-FINDR



PPI VCA AMEE

16

WP4 – First test

Sensor

ProductSpatial resolution

Spectral resolution 10 nm

HYPERION (EO-1)

L1R (no geometric correction)30m

Geographic area South Sicily

Acquisition date 22-07-2001

5

10

15

19* CC NN

N. of channels 175

Sub-image200x200 pixels (~6Km x

6Km)

400 600 800 1000 1200 1400 1600 1800 2000 2200 24000

1

2

3

4

5

6

7

x 106

(nm) wavelength

radi

anza

spe

ttral

e

33eN

510fP

Endmembers positions

Endmembers spectra

NWHFC with

Unsupervised clustering (WP4A)

Unsupervised endmembers extraction (WP4B)



• 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

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: Applicative products



UNMIXING ACCURACYPrisma-like data RE%=5.03

Endmember diff.Shrubs 3.2%Beech 1.56%Grassl. 1.67%

WP5A: Land degradation and natural vegetation monitoring



150 m.

484 pixels

25 pixels

22.500 m2

Hyperion Classification

HYP high spatial resolution

PRISMA like

VHR

GSD 1.5 m

GSD: 7 m

GSD: 30 m

Rock outcropShrubs (3222)

Arid grassland (3211)

Beech forest (3115)

Classification of natural areas up to the 4th Corine level for MIVIS and Hyperion (subpixel) on the Pollino National Park


SAP4PRISMA2012 Munich IGARSS, 22-27 July

WP5A: Ecosystem analysis and vegetation health status

ij

ij

pmin

p=SHAPE

Measures the joint edges of the patch and is connected with the level of naturalness of the cover:

- High natural: edges articulated - Low natural: smooth edges

The influence of human activities increase the regularity of edges (e.g. forest near cultivated land)

Measures the complexity of the shape of the patch over a range of spatial scales assessing at the same time the configuration of the perimeter and the size of the block considered.

High levels of FRACT, for very small plugs, may give an indication of fragmentation processes in place

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)

pij is the perimeter of patch ij

min pij min is the minimum

perimeter possible pij for a

figure having the area of the

patch ij

aij is the area of the patch ij ij

ij

aln

)p0.25(2ln=FRACT

Forested area

shore

Salt contamination limitDuneForested area

shore

Salt contamination limitDune



Data integration:• Satellite (including

Hyperspectral) based landscape metrics

• Geophysical surveys• Chemical-physical

measurements

15B

17B

2B

11B

14A

1987- 2004

FRACT index concerns the patch regularity

Negative trends i.e. an increase of the shape regularity indicates for a decrease of naturality

Positive trends provides an indication of ongoing fragmentation processes

Fract and Coastal variations

WP5A: Land degradation and natural vegetation monitoringExample of saltwater intrusion

WP5A: Land degradation: soil quality and soil degradation – ongoing activities (organic matter, CaCO3, iron content, salinity, etc.)



Mean value = 0.361St. Dev. = 0.107

very fine sand

fine sand

silt

GS

I

Grain size (micron)

Mixing Soil – NPVMixing Soil – PVGSI ± 1

Soil percentage

Lab experiments for soil texture analysis

Spectral Index vs unmixing for soil erosion

9/7/2007 26/6/2012



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

Dataset Variable Mean ± st.dev Min Max Skewness

B071 132

samples

clay 38.9 ± 9.2 15.3 56.1 -0.18

silt 19.2 ± 3.7 8.4 28.9 0.36

sand 41.9 ± 10.9 15.0 62.0 -0.12

Maccarese, Central ItalySamples were collected in two fields from the 0-30 cm layer by means of a gouge auger

Airborne MIVIS

CHRISSoil sample collection

Lab analysis (clay, silt, sand)

Remote sensing data acquisitions: MIVIS & CHRIS

Soil point measurements

Krigingvalues

Calibration PLSR models (B071B or random) Validation B071A

field or random

RMSE: root mean squared error R

RPD: ratio of performance to deviation RPD>2 accurate modelsRPD between 1.4 and 2 intermediateRPD<1.4 no predictive abilityChang and Laird (2002)

WP5B - Scientific and application tasks for agriculture Soil components at field scale: preliminary results



SAP4PRISMA

Block kriging CHRIS-PROBA MIVIS



Calibration: 468Validation: 390

CHRIS – B071B x B071A CHRIS – random

MIVIS – B071B x B071A MIVIS – random

Calibration: 6435Validation: 4771

WP5B - Scientific and application tasks for agriculture Soil components at field scale: preliminary results

1 Ju

ly C

HR

IS

26 J

uly

CH

RIS

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)

LAI Biomass BiomassLAI



WP5B - Scientific and application tasks for agriculture Crop components: preliminary results



Identification, monitoring and possible quantification of pollutants through specific spectral features relatable to changes in chemical composition of the polluted soil

Analysis and optimization of methods and algorithms for the estimation of soil/water pollution due to human activities and natural hazards according to the PRISMA sensors’ characteristics



WP5C: Applications for the management of natural and human-induced hazards

549680

549680

RDI

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

Airborne Hyp image: Red Dust dispersion map as attained by applying SFF algorithm.Yellow depicts low-medium RD surface concentration, red represents high RD surface concentration.



WP5C: Applications for the management of naturaland human-induced hazards - Damage severity index (post fire)

Burn Severity Scale

No damage Low Moderate High

0 0.5 1 1.5 2.0 2.5 3.0

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;

2. Construction of the index based on the results obtained by simulations and calibration based on real image data.

3. Development of an algorithm for the automatic calculation of the index





Conclusions and Future work

The SAP4PRISMASAP4PRISMA 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:

PRISMA missionPRISMA mission will provide major increase of systematic HYP acquisition capacities with significant spectral performances so enabling a major qualitative/quantitative step in services provided




• Soil erosion assessment and monitoring of Land degradationLand 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 monitoringCrop 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 mappingDisaster mapping: identification and quantification of surface pollutants through their specific spectral signatures or specific features (changes in chemical composition of polluted soils); damage severity index (post fire) development




• 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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DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTRAL PRISMA MISSION: THE...

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