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DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTRAL PRISMA MISSION: THE SAP4PRISMA PROJECT
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DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTRAL PRISMA MISSION: THE SAP4PRISMA PROJECT

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  • Analysis of CCP Topic: 1. Definition of “boutique” satellites 2. Identification of current “boutique” satellites 3. Identification of current “boutique” satellite markets 4. Identification of potential “boutique” satellite markets 5. Develop a profitable service based on 2&4

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  • 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 • ConclusionsPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 3. PRISMA - context and backgroundMission 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 forenvironmental 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 HypseoPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 4. Mission highlightsCoverage: 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 imgSystem Latencies (inside AoI): Re-look time: < 7 days Response time: < 14 daysMission modes: PRISMA Hyperspectral Primary: User driven sensor utilizes prisms to obtain the dispersion of Secondary: Data driven (background mission) incoming radiation on a 2-DLife time: matrix detectors 5 yearsPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 > 0.20PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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)PRISMA missionSAP4PRISMA prj 6WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 risksPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 developmentPRISMA missionSAP4PRISMA prj 8WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 definedPRISMA missionSAP4PRISMA prj 9WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 PSFPRISMA missionSAP4PRISMA prj 10WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 algorithmsG. 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, 2011PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 reductionPRISMA missionSAP4PRISMA prj 12WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 =1PRISMA missionSAP4PRISMA prj 13WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 )PRISMA missionSAP4PRISMA prj 14WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 HFCPRISMA missionSAP4PRISMA prj 15WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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 positionsPRISMA missionSAP4PRISMA prj 16WP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 17. WP5: Applicative productsThe overall objective of this WP is the developmentof PRISMA data applications that are feasible, useful and innovative to meetthe needs of end users interested in agriculture, land degradation andthe 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 hazardsPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 18. WP5A: Land degradation and natural vegetation monitoring Rock outcrop Classification ofGSD 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 HyperionVHR 150 m. (subpixel) on theGSD: 7 m 484 pixels Pollino National Park UNMIXING ACCURACYHYP 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%PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 19. WP5A: Ecosystem analysis and vegetation health statusAccurate natural vegetation monitoring procedures including multi-temporal and multi-sensor datato understand its distribution useful in the landscape metrics analysis (block level classification) p ijSHAPE = 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 lnaijMeasures the joint edges of the patch and is Measures the complexity of the shape of the patchconnected 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, mayThe influence of human activities increase the give an indication of fragmentation processes inregularity of edges (e.g. forest near cultivated land) placePRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 20. WP5A: Land degradation and natural vegetation monitoring Example of saltwater intrusion Data integration:FRACT index concerns the • Satellite (includingpatch regularity Hyperspectral) basedNegative trends i.e. an landscape metricsincrease of the shape Fract and Coastal • Geophysical surveysregularity indicates for a variations • Chemical-physical measurementsdecrease of naturalityPositive trends provides an 1987- 2004indication of ongoing 15Bfragmentation processes 17B 2B 11B 14A Forested area Salt contamination limit Dune shorePRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 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 sandGSI fine sand Soil percentage Grain size (micron) Mean value = 0.361 St. Dev. = 0.107 9/7/2007 26/6/2012 PRISMA mission SAP4PRISMA prj WP5 activities 2012 Munich IGARSS, 22-27 July Conclusion
  • 22. WP5B: Scientific and application tasks for agriculture:Development and improvement of algorithms and methods for estimating from HYS dataSoil propertiesBiophysical and biochemicalvariables of agriculturalcropsVariables of agronomic andenvironmental interest,through the assimilation ofremote sensing data intoworking modelsPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 23. WP5B - Scientific and application tasks for agriculture Soil components at field scale: preliminary resultsSamples were collected in two fields from the 0-30 cm layer by Maccarese, Central Italymeans 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 randomPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 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: 4771PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 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)PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 JulyConclusion
  • 26. WP5C: Applications for the management of natural and human-induced hazardsIdentification, monitoring and possible Airborne Hyp image:quantification of pollutants through Red Dustspecific spectral features relatable to dispersion map as attained bychanges in chemical composition of applying SFFthe polluted soil algorithm. Yellow depicts low-mediumAnalysis and optimization of methods RD surfaceand algorithms for the estimation of concentration, red representssoil/water pollution due to human high RDactivities and natural hazards surface concentration.according to the PRISMA sensors’characteristics λ 680 − λ 549 RDI = λ 680 + λ 549Distribution maps of pollutantsValidation/Calibration of the methodologies andproducts and Detection Limit assessment of mainpollutants spectral absorptions features on thePRISMA spectral sampling and noise characteristicsPRISMA missionSAP4PRISMA prjWP5 activities activities 2012 Munich IGARSS, 22-27 July 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 27. WP5C: Applications for the management of natural and human-induced hazards - Damage severity index (post fire)Build an index able to estimate theseverity of the damage in burnedareas.The work will be developed in threemain 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 High5. Development of an algorithm for the 0 0.5 1 1.5 2.0 2.5 3.0 automatic calculation of the indexPRISMA missionSAP4PRISMA prjWP5 activities activities 2012 Munich IGARSS, 22-27 July 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 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:PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 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) developmentPRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion
  • 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!!PRISMA missionSAP4PRISMA prjWP5 activities 2012 Munich IGARSS, 22-27 July SAP4PRISMAConclusion