Asam, Sarah

Bachmann, Martin

Kuehl, Kevin

Marshall Ingram, David

Schwarzenbacher, Frederic

Institute of Transport Research:Publications

Martin Bachmann1, David Marshall1, Kevin Kühl1, Frederic Schwarzenbacher 2 and Sarah Asam11 DLR-EOC Earth Observation Center2 Topodat®, Koenig, Schwarzenbacher & Will GbRLINEAR SPECTRAL UNMIXING FOR LARGE SPACEBORNE HYPERSPECTRAL DATASETS -CHALLENGES AND SOLUTIONS FOR THE AUTOMATED DLR FCOVER CHAIN Rationale▪ Linear spectral unmixing can be highly accurate▪ If all End-Members (EM) are known▪ If spectral variability of EMs is included in mixture model (MESMA)Azerbaijan,Degradation of grasslandsRationale▪ Linear spectral unmixing is highly accurate▪ If all End-Members (EM) are known▪ If spectral variability of EMs is included in mixture model (MESMA)▪ But in real-life:▪ Not all EM are known▪ If strictly tile-based: EM change between tiles▪ Some EM are already mixtures▪ Some combinations of EM result in ill-conditioned mixing model▪ View angle effects generally limit accuracyRationale▪ Linear spectral unmixing can be highly accurate▪ If all End-Members (EM) are known▪ If spectral variability of EMs is included in mixture model (MESMA)▪ But in real-life:▪ Not all EM are known▪ If strictly tile-based: EM change between tiles▪ Some EM are already mixtures▪ Some combinations of EM result in ill-conditioned mixing model▪ View angle effects generally limit accuracy▪ Resulting in:▪ Reduced overall accuracy▪ Variable accuracy over scene▪ Bordering effects between tilesBackground – Linear Spectral Unmixing▪ Linear Spectral Mixture Model Where amn: reflectance of EM n in band mbm:  measured reflectance in band mxm:  abundance for EM n A: m*n EM-matrixx: abundance vector for n EMb: measured spectrum in m bands Overdetermined problem, solving by Least-Squaresapproximation, e.g. where                                      plus constraints (sum-to-one, non-neg.)Background – Linear Spectral Unmixing▪ Linear Spectral Mixture Model Where amn: reflectance of EM n in band mbm:  measured reflectance in band mxm:  abundance for EM n A: m*n EM-matrixx: abundance vector for n EMb: measured spectrum in m bands Overdetermined problem, solving by Least-Squaresapproximation, e.g. where                                      plus constraints (sum-to-one, non-neg.)Background – Linear Spectral Unmixing▪ Linear Spectral Mixture Model Where amn: reflectance of EM n in band mbm:  measured reflectance in band mxm:  abundance for EM n A: m*n EM-matrixx: abundance vector for n EMb: measured spectrum in m bands Overdetermined problem, solving by Least-Squaresapproximation, e.g. where                                      plus constraints (sum-to-one, non-neg.)Background – Linear Spectral Unmixing▪ Linear Spectral Mixture Model Where amn: reflectance of EM n in band mbm:  measured reflectance in band mxm:  abundance for EM n A: m*n EM-matrixx: abundance vector for n EMb: measured spectrum in m bands Overdetermined problem, solving by Least-Squaresapproximation, e.g. where                                      plus constraints (sum-to-one, non-neg.)Multiple Endmember Spectral Mixture AnalysisParticular EMs used to model a pixel and number of EMs (i.e., matrix A)varies on a per-pixel basisUsually model with smallest RMS error selectedMore materials and spectral variability of EM includedBackground – Accuracy of Image EMs▪ Test case based on synthetic scenes(pure field spectra from 3 campaigns in Spain, Namibia plus: MEDSPEC, JHU SpecLib, ECOSTRESS)▪ SMACC (Sequential max. angle convex cone) on pre-segmented image▪ Additional EMs included after 1st unmixing iteration▪ ~70% of all EM could be automatically retrievedBut: incorrect spectra also detected!  ▪ Subsequent automated EM classification in 3 classes (PV – NPV – Soil)▪ In simulations:~93% correctly classified EM▪ Consequences:30% of EM missing~1 in 10 EM is incorrect   ▪ Usually an incorrect and incomplete mixture modelChallenges & Solutions (I)▪ Now – what can we do to improve the situation?▪ Take advantage of EM-derivation over multiple tiles: SSEE (Rogge et al.)▪ Exclude linear dependencies in mixture model▪ Select mixture models which are “reasonable”▪ Identify mixture models which are likely to be wrong▪ Address general limitations - view angle effects▪ Use an appropriate shade component▪ Improve solving algorithms▪ …… and explicitly include a per-pixel reliability score▪ Presenting one approach: automated fCover unmixingAddressing Numerical Problems▪ Matrix inversion may result in numerical problems!▪ Linear dependencies between spectrally similar classes▪ Higher number of EM – higher probability of linear dependencies▪ Result: ill-conditioned problemThus: check condition number κ of EM-Matrix A:κ = ||A|| ||A-1||     where || denotes the euklidian L2-nornExclude EM combinations which result in ill-conditioned problemFor the mentioned SpecLibs: >10% of all mixing models(esp. combination of Dry Veg. – Soil)Selecting Reasonable Mixture ModelsModel selection in standard MESMA: RMS Error (i.e., goodness of fit)Some improvements in µMESMA:▪ Integrate information from spatial dimensionSoil type -and thus soil EM- unlikely to change between pixels.Thus: check, if unmixing error significantly increases when using dominant soil EM in spatial neighborhood – Automated residual analysis– Check residual spectra for diagnostic absorption features– Identify & parameterize these featuresIncreased stability against incorrect EM Take advantage of spectroscopic dataIdentify Error-Prone Pixels▪ Linear Spectral Unmixing already offers measure for “goodness of fit”, i.e. the model RMSE▪ Improved detection of pixels which are likely error-prone based on:▪ Residual analysis & weighted model RMSE▪ Agreement with empirical regression models between cover % and band indices▪ Critical local incidence angle▪ L2 data quality flags (from pre-processing)R2 ~0.73, n= 61 tests, 61.000 modelsBaseline for a Reliability ScoreEndmemberExtractionSpectralUnmixingOutput GenerationEndmemberIdentificationC-FactorGenerationData PreparationMask erroneous pixels, mask unwanted materials, filter imageDetect and extract potentially pure pixel spectraCluster and identify these spectra, group into PV, NPV, SOIL, and remove redundant spectraCalculate ground cover fractions („abundances“) for PV, NPV, SOILGenerate additional unmixing-related products(classification by EM maps, reliability, statistics, …)„Translate“ abundance images into C-FactorsEndmemberExtractionSpectralUnmixingOutput GenerationEndmemberIdentificationC-FactorGenerationData Preparation„Garbage pixcollection“ (prepare_smacc)Masking of unwantedmaterials (GUF)Spatial-SpectralFiltering (OIAS / SavGol)Pre-Clustering (MSBP)Spectral Identification(RandomForest)EM RedundancyChecks (which_em)Spatial-Spectral EM Extraction (SSEE)Linear SpectralUnmixing(µMESMA)Generate outputs(scale_after_unmix , local_incidence, …)Soil degradationmodelsReconstruct_Soil, SOM, …EndmemberExtractionSpectralUnmixingOutput GenerationEndmemberIdentificationC-FactorGenerationData Preparation„Garbage pixcollection“ (prepare_smacc)Masking of unwantedmaterials (GUF)Spatial-SpectralFiltering (OIAS / SavGol)Pre-Clustering (MSBP)Spectral Identification(RandomForest orspec_class)EM RedundancyChecks (which_em)Spatial-Spectral EM Extraction (SSEE)Generate outputs(scale_after_unmix , local_incidence, …)Soil degradationmodelsReconstruct_Soil, SOM, …Linear SpectralUnmixing(µMESMA)Configuration:- Solver: unconstrained, bound-constr., weighted- Optimization criteria: RMSE, residual analysis, colinearity check, combined- Components: 2, 3 & 4-EM, shade component, sim. soil shading- Inclusion of spatial neighborhood information- Correction of local incidence angle (model)- Sensor-optimization: bad-band-lists, smoothing, ….- Reliability score … but wait:can DESIS separate between NPV & soil ?Absorption features ofA: Xylan & Cellulose B: Lignin & CelluloseC: CelluloseD: ClayE: CarbonatesVegetation without Chlorophyll=> NPV=> Soil… but wait:can DESIS separate between NPV & soil ?Absorption features ofA: Xylan & Cellulose B: Lignin & CelluloseC: CelluloseD: ClayE: CarbonatesVegetation without Chlorophyll=> NPV=> Soil… but wait:can DESIS separate between NPV & soil ?DESISsingle pixelspectraofPV (pre-proc), NPV & soilfCover for DESIS data – simulations▪ Validation using simulatedfractal scenesfCover for DESIS – Azerbaijan ResultsNote:• Field measurements: 296 samples fromAugust & Oct. 2018 (different years!)• Living biomass, pot. vegetation & otherparameters sampled,so MESMA classes are combinedfCover for DESIS – Azerbaijan Results MAE SD of absolute errors PV (run 1 – 2EM) 8.13 10.97 PV scaled (run 1 – 2EM) 8.52 7.63 PV (run 2 – 3EM) 11.74 11.80 PV scaled (run 2 – 3EM) 13.88 14.32  1 Note:• Field measurements: 296 samples fromAugust & Oct. 2018 (different years!)• Living biomass, pot. vegetation & otherparameters sampled,so MESMA classes are combinedSummary – challenges and solutions▪ EM variability and inherent between-class EM similarity▪ MESMA & check for ill-conditioned mixture models▪ EM derivation in large datasets▪ SSEE spawning multiple tiles▪ EM labeling using RF classifiers trained on field SpecLibs▪ … but it‘s never 100% accurate !▪ Identification of likely incorrect abundances▪ Include per-pixel reliability model and residual analysis▪ Soil & NPV separation with DESIS▪ Indeed some potential, but better combine after unmixing to PV-cover ▪ EnMAP, PRISMA and EMIT cover the SWIR with diagnostic NPV & soil features▪ EM labeling, reduction of EM set <=> computation times▪ Work in progress…Name des Vortragenden, Institut, DatumName des Vortragenden, Institut, DatumLand Degradation – Sustainable Development Goal (SDG)SDG decided by the UN General Assembly in September 2015, cameinto effectJanuary2016Land Degradation – SDG 15.3▪ fCover – Products & applicationsProducts & Applications: Cabo de Gata (PhD Martin)Bedeckungsgrad – PV Bedeckungsgrad – NPV Bedeckungsgrad – Boden Bewertung der GüteProducts & Applications: Cabo de Gata (PhD Martin)Anteil an offenliegendem Boden… ohne Korrektur … mit empirischer Korrektur   Lokaler Einfallswinkel – Übergang zu bewegtem Relief Products & Applications: Cabo de Gata (PhD Martin)▪ Validierung:▪ Nutzung spektraler Bibliotheken zur Simulation von ~80.000 Mischmodellen Feldmessungen aus CdG, Calanas, Namibia, MEDSPEC (JRC), USGS & Johns Hopkins U.Products & Applications: Cabo de Gata (PhD Martin)▪ Validierung:▪ Feldmessungen an 9 vorstratifiz. Tesflächenaus je 40-80 Einzelmessungen Products & Applications: Cabo de Gata (PhD Martin)▪ Validierung:▪ Feldmessungen an 9 vorstratifiz. Tesflächenaus je 40-80 Einzelmessungen Products & Applications: Aserbaidschan (w. Sarah & David)Aserbaidschan,Degradation of grasslandsEnMAP fCoverProzessor

Linear Spectral Unmixing for Large Spaceborne Hyperspectral Datasets - Challenges and Solutions for the Automated DLR FCover Chain

https://elib.dlr.de/198992/1/Bachmann_fcover_WHISPERS_final.pdf

Linear Spectral Unmixing for Large Spaceborne Hyperspectral Datasets - Challenges and Solutions for the Automated DLR FCover Chain

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