Ieee gold 2010 resta


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  • Nel contesto dell’elab di dati ipersp il concetto di rango coincide con la definizione recent fornita in lett di vd
  • Le componenti della base a rango K vengono determinate utilizzando , l2 infinito
  • Di seguito si riporta in sintesi uno schema a blocchi dell’algoritmo
  • Si riportano infine i risultati associati alla stima della vd del dato indian pine … in particolare il dato è costituito da 16 classi distinte
  • Riepilogando il lavoro effettuato … concentrandosi sui vincoli
  • Ieee gold 2010 resta

    1. 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
    2. 2. 1/10 Outline <ul><li>Introduction </li></ul><ul><ul><li>Dimensionality Reduction (DR) in Target Detection Applications </li></ul></ul><ul><li>Analysis and development of DR techniques </li></ul><ul><ul><li>State of the art </li></ul></ul><ul><ul><li>Innovative Technique </li></ul></ul><ul><li>Performance Evaluation </li></ul><ul><ul><li>Analysis on a case study </li></ul></ul><ul><ul><li>Analysis of computational cost </li></ul></ul><ul><li>Conclusions </li></ul><ul><ul><li>Application of the proposed work </li></ul></ul><ul><ul><li>Further developments </li></ul></ul>Remote Sensing & Image Processing Group
    3. 3. 2/10 <ul><li>The generic sample, or pixel, of the hyperspectral data can be modeled as the combination of a signal contribution and a noise contribution. </li></ul><ul><li>The signal is modeled according to the </li></ul><ul><li>Linear Mixture Model (LMM) [Stein,02] . </li></ul>N c x 1 <ul><li>Hyperspectral sensors are characterized by a very high number of spectral bands and a very accurate spectral resolution . </li></ul>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)
    4. 4. Anomaly Detection & Rare Vectors 3/10 Rare Vectors are often spectral components of the target of interest Anomaly Detection (AD) <ul><li>No a-priori hypothesis about the target is assumed </li></ul><ul><li>The goal is to identify those pixels having a spectral signature which is significantly different from the background </li></ul>Remote Sensing & Image Processing Group <ul><li>Surveillance of strategically sensible areas </li></ul><ul><li>Change Detection in operative areas </li></ul><ul><li>Mine Detection in terrestrial and sea environment </li></ul><ul><li>Shipwreck survivor location </li></ul>Applications Rare Vectors Scarcely represented in the observed data Linearly independent on the abundant vectors which address the background
    5. 5. 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 <ul><li>DR typically includes two distinct steps: </li></ul>Projection of the original data onto the estimated subspace 4/10 <ul><li>Dimensionality Reduction (DR) goals: </li></ul>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
    6. 6. <ul><li>Traditional DR Techniques do not perform well in the presence of rare vectors </li></ul><ul><li>Optimality criterion oriented to rare vectors preservation [Kuybeda,07] . </li></ul>MX - SVD IRVE MOCA IRVE - SRRE Basis Estimation Algorithms DR Algorithms Suboptimal Solution <ul><li>Traditional DR Techniques are based on the analysis of second order statistics </li></ul>PCA ITC [Stoica,04] 5/10 Remote Sensing & Image Processing Group Traditional Methods Drawbacks – New Optimality Criterion
    7. 7. 6/10 Rank estimation of the abundant vectors subspace Singular Value Decomposition IRVE - SRRE <ul><li>Subsequently a linear transformation is applied to identify the subspace which address the background. </li></ul><ul><li>The original data is first normalized with respect to the estimated covariance matrix of the noise and an estimate of the rank of the abundant vector subspace is obtained. </li></ul><ul><li>Finally the IRVE-SRRE algorithm is applied providing the rare vectors subspace rank and components estimation. </li></ul>rare vectors background Remote Sensing & Image Processing Group IRVE Algorithm – Statistical Rare Rank Estimator (SRRE)
    8. 8. Rare Vector Original Data Energy BIC - PCA RGB image Indian Pine <ul><li>DR on a case study </li></ul>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
    9. 9. VD AIC = 108 VD MDL = 21 VD GIC = 39 VD IRVE-SRRE = 25 VD MOCA = 23 <ul><li>VD estimation on a case study </li></ul><ul><li>Computational load evaluation </li></ul>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
    10. 10. <ul><li>Traditional DR methods can reveal some inadequacy to preserve rare vectors representation. </li></ul><ul><li>Analysis of DR methods aimed at preserving rare vectors which can be </li></ul><ul><li>spectral components of the target of </li></ul><ul><li>interest. </li></ul><ul><li>Development of a new method oriented to rare vectors preservation which is very efficient from a computational point of view </li></ul><ul><li>Exaustive performance evaluation introduced by the new techniques on existing target detection algorithms. </li></ul>9/10 Open research topic Remote Sensing & Image Processing Group Conclusions
    11. 11. 10/10 <ul><li>[Ste02] D. W. J. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, A. D. Stocker, “Anomaly Detection from Hyperspectral Imagery”, IEEE Signal Process. Mag., 19(1), 58-69 (2002). </li></ul><ul><li>[Ric93] J. A. Richards, X. Jia, Remote Sensing Digital Image Processing, 9, Springer-Verlag, 1993. </li></ul><ul><li>[Aci08] N. Acito, G. Corsini, M. Diani, S. Matteoli, S. Resta, “A novel technique for hyperspectral signal subspace estimation in target detection applications ”, Accepted for International Conference on Geoscience and remote sensing – IGARSS, 2008. </li></ul><ul><li>[Kuy07] O. Kuybeda, D. Malah and M. Barzohar, ”Rank estimation and redundancy reduction of high dimensional noisy signals with preservation of rare vectors”, IEEE Signal Processing Magazine, vol. 55, Issue 12, Decemder 2007, pp. 5579-5592. </li></ul><ul><li>[Cha04] C. I. Chang and Q. Du, “Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery”, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 3, March 2004. </li></ul><ul><li>[Sto04] P. Stoica and Y. Selen, “Model order selection: a review of information criterion rules”, IEEE Signal Processing Magazine, vol. 21, Issue 4, July 2004, pp. 36-47. </li></ul><ul><li>[Rog96] R. E. Roger and J. F. Arnold, “Reliably estimating the noise in AVIRIS hyperspectral imagers” Int. J. Remote Sens., vol. 17, no. 10, pp. 1951–1962, 1996. </li></ul>Thank you for the attention! Remote Sensing & Image Processing Group References