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Signal Subspace Estimation in Hyperspectral Data for Target Detection Applications 2010 IEEE GOLD REMOTE SENSING CONFERENCE Salvatore Resta , Nicola Acito, Marco Diani, Giovanni Corsini   Dipartimento di Ingegneria dell’Informazione, Università di Pisa via G. Caruso 16, 56122 Pisa, Italy 29, 30 April 2010 Accademia Navale, Livorno, Italy
1/10 Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Remote Sensing & Image Processing Group
2/10 ,[object Object],[object Object],[object Object],N c x 1 ,[object Object],Spectral Dimension Hyperspectral Data Image intensity for a fixed wavelenght Remote Sensing & Image Processing Group Hyperspectral Data Analysis Spectral Signature of the pixel Wavelenght (nm)
Anomaly Detection & Rare Vectors 3/10 Rare Vectors are often spectral components of the target of interest Anomaly Detection (AD) ,[object Object],[object Object],Remote Sensing & Image Processing Group ,[object Object],[object Object],[object Object],[object Object],Applications Rare Vectors Scarcely represented in the observed data Linearly independent on the abundant vectors which address the background
Dimensionality Reduction (DR) Determination of the Virtual Dimensionality (VD), which is the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification [Chang,04]. Rank Estimation Basis Estimation ,[object Object],Projection of the original data onto the estimated subspace 4/10 ,[object Object],Rare Vectors preservation in Target Detection Applications Remote Sensing & Image Processing Group Dimensionality Reduction Computational complexity reduction Preservation of major characteristics in the observed data
[object Object],[object Object],MX - SVD IRVE MOCA IRVE - SRRE Basis Estimation Algorithms DR Algorithms Suboptimal Solution ,[object Object],PCA ITC [Stoica,04] 5/10 Remote Sensing & Image Processing Group Traditional Methods Drawbacks – New Optimality Criterion
6/10 Rank estimation of the abundant vectors subspace Singular Value Decomposition IRVE - SRRE ,[object Object],[object Object],[object Object],rare vectors background Remote Sensing & Image Processing Group IRVE Algorithm – Statistical Rare Rank Estimator (SRRE)
Rare Vector Original Data Energy BIC - PCA RGB image Indian Pine ,[object Object],MOCA IRVE - SRRE Residual Energy Projection Matrix 7/10 Maximum Value of residual energy Remote Sensing & Image Processing Group Experiments on a case study RESIDUAL ENERGY RESIDUAL ENERGY RESIDUAL ENERGY BIC - PCA MOCA IRVE - SRRE 80000 329 321
VD AIC = 108 VD MDL = 21 VD GIC = 39 VD IRVE-SRRE = 25 VD MOCA = 23 ,[object Object],[object Object],Computational load of IRVE – SRRE algorithm is considerably reduced with respect to MOCA algorithm Traditional methods show a tendency to overestimate the subspace rank 8/10 Remote Sensing & Image Processing Group Experiment on a case study & Computational Complexity ITC – PCA MOCA IRVE - SRRE 154 s 690 s 64 s Computational load Indian Pine
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],9/10 Open research topic Remote Sensing & Image Processing Group Conclusions
10/10 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Thank you for the attention! Remote Sensing & Image Processing Group References

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Ieee gold 2010 resta

  • 1. Signal Subspace Estimation in Hyperspectral Data for Target Detection Applications 2010 IEEE GOLD REMOTE SENSING CONFERENCE Salvatore Resta , Nicola Acito, Marco Diani, Giovanni Corsini   Dipartimento di Ingegneria dell’Informazione, Università di Pisa via G. Caruso 16, 56122 Pisa, Italy 29, 30 April 2010 Accademia Navale, Livorno, Italy
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Hinweis der Redaktion

  1. Nel contesto dell’elab di dati ipersp il concetto di rango coincide con la definizione recent fornita in lett di vd
  2. Le componenti della base a rango K vengono determinate utilizzando , l2 infinito
  3. Di seguito si riporta in sintesi uno schema a blocchi dell’algoritmo
  4. Si riportano infine i risultati associati alla stima della vd del dato indian pine … in particolare il dato è costituito da 16 classi distinte
  5. Riepilogando il lavoro effettuato … concentrandosi sui vincoli