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Pisa, 30.11.2007 Stefania Matteoli  a Nicola Acito  b Marco Diani  a Giovanni Corsini  a a  Dipartimento di Ingegneria del...
Background - Hyperspectral Target Detection
Outline
Linear Mixing Model (LMM) <ul><ul><li>random vector associated to the test pixel </li></ul></ul><ul><ul><li>spectral signa...
Subspace-based target detection scheme original data space <ul><ul><li>Background is suppressed via orthogonal projection ...
Target detection scheme, detection performance Background suppression <ul><ul><li>Target detection performance (P D  and P...
Global background vs local background Global  background subspace Local  background subspace generally Target detection Ba...
Global background vs local background Global  approach Local  approach <ul><li>Global background lies in a high-dimensiona...
Global background estimation : N-S NWHFC SVD all image pixels N-S subspace dimension (Virtual Dimension, VD) basis vectors...
Local background estimation : LBSS local neighborhood LBSS <ul><li>A set of neighboring pixels is let span the background ...
Local background estimation : a new algorithm local neighborhood LBSE <ul><li>Automatic technique </li></ul><ul><li>Adapti...
LBSE procedure
Target detection step each considered background basis estimation algorithm (N-S, LBSS, and LBSE) should be embodied in a ...
Results: 1) LBSE adaptability to spatially variable backgrounds True-color image LBSE  map LBSE  map   histogram urban are...
Results: 2) Simulation methodology N-S, LBSS, LBSE the scalar value allows to set a desired value of the target residual e...
Results: 2) Simulation results (1000 images) Data-set
Results: 2) Simulation (1000 images),  [email_address] D =1 LBSS:  K L BSS  is a user-specified parameter. No criteria exi...
Results: 3) Testing on real data real target detection scenario with ground-truthed targets LBSE histogram ROC curves <ul>...
Conclusion LBSE <ul><li>novel  and  fully automatic  algorithm for  local  background subspace estimation and suppression ...
Pisa, 30.11.2007 Livorno, 30.04.2010 Thanks for your attention! South of Italy Chapter
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Matteoli ieee gold_2010_clean

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  • As regards background suppression, background subspace can be defined globally over the whole scene, or locally, by considering a neighborhood of each test pixel.
  • That is the image where the targets have been inserted. The simulation was conducted for several different values both of FI and ALFA:
  • These are detection results. FAR@PD=1, so the lower the better. Specifically we have plots of FAR vs FI and FAR vs ALFA. Results are in favor of the local approach. The global method (blue curve) manages to perform comparably to the local ones for a value of FI=10dB, but its performance quickly degrades for lower FI. (Of course the performance gets better as ALFA increases). Conversely, the local methods manage to detect the target even if it has very low global residual energy. Specifically, LBSE (red curve) provides the best results in most cases. LBSS exhibits a diversity in performance wrt to the number of neighboring pixels considered, for the selection of which no criteria can be invoked. IN fact, here, several configurations had to be tested so as to get good performance.
  • Transcript of "Matteoli ieee gold_2010_clean"

    1. 1. Pisa, 30.11.2007 Stefania Matteoli a Nicola Acito b Marco Diani a Giovanni Corsini a a Dipartimento di Ingegneria dell’Informazione, Università di Pisa, Pisa, Italy b Accademia Navale, Livorno, Italy Livorno, 30.04.2010 Hyperspectral Target Detection via Local Background Suppression South of Italy Chapter Remote Sensing & Image Processing Group
    2. 2. Background - Hyperspectral Target Detection
    3. 3. Outline
    4. 4. Linear Mixing Model (LMM) <ul><ul><li>random vector associated to the test pixel </li></ul></ul><ul><ul><li>spectral signature of the target </li></ul></ul><ul><ul><li>scalar value accounting for sub-pixel targets </li></ul></ul><ul><ul><li>matrix spanning the background subspace </li></ul></ul><ul><ul><li>vector of background components </li></ul></ul><ul><ul><li>background subspace of dimension </li></ul></ul><ul><ul><li>zero-mean Gaussian random noise vector with covariance matrix </li></ul></ul><ul><ul><li>number of spectral bands </li></ul></ul>
    5. 5. Subspace-based target detection scheme original data space <ul><ul><li>Background is suppressed via orthogonal projection </li></ul></ul>background suppression <ul><ul><li>projection matrix onto </li></ul></ul>residual subspace target detection
    6. 6. Target detection scheme, detection performance Background suppression <ul><ul><li>Target detection performance (P D and P FA ) depends on </li></ul></ul>noise covariance matrix target residual energy P D is expected to be an increasing function of . Target detection
    7. 7. Global background vs local background Global background subspace Local background subspace generally Target detection Background suppression
    8. 8. Global background vs local background Global approach Local approach <ul><li>Global background lies in a high-dimensional subspace </li></ul><ul><li>Low target residual energy after suppression (major risk of target leakage ) </li></ul><ul><li>Provides lower-dimensional subspaces </li></ul><ul><li>Higher residual energy after projection (which benefits to detection performance) </li></ul>Target detection Background suppression <ul><ul><li>The background subspace basis is unknown and has to be estimated from the data </li></ul></ul>
    9. 9. Global background estimation : N-S NWHFC SVD all image pixels N-S subspace dimension (Virtual Dimension, VD) basis vectors <ul><li>N-P based test on covariance and correlation matrix eigenvalues </li></ul><ul><li>based on asymptotic properties </li></ul>Target detection Background suppression
    10. 10. Local background estimation : LBSS local neighborhood LBSS <ul><li>A set of neighboring pixels is let span the background subspace </li></ul>Local Background Subspace Selection <ul><li>The local subspace dimension is imposed by the number of neighboring pixels </li></ul><ul><li>LBSS cannot account for background spatial variability within the scene! </li></ul>LBSS main limitation <ul><li>target leakage (overestimation) </li></ul><ul><li>false alarms (underestimation) </li></ul>Target detection Background suppression
    11. 11. Local background estimation : a new algorithm local neighborhood LBSE <ul><li>Automatic technique </li></ul><ul><li>Adaptive estimation on a per-pixel basis </li></ul><ul><li>Estimated local background subspace tailored to background spatial variability </li></ul>Local Background Subspace Estimation Statistical hypothesis testing SVD LBSE local subspace dimension basis vectors Target detection Background suppression
    12. 12. LBSE procedure
    13. 13. Target detection step each considered background basis estimation algorithm (N-S, LBSS, and LBSE) should be embodied in a subspace-based target detector Generalized Matched Filter (GMF) defined in the orthogonal complement of the background subspace N-S LBSS LBSE Background suppression Target detection
    14. 14. Results: 1) LBSE adaptability to spatially variable backgrounds True-color image LBSE map LBSE map histogram urban area: high complexity vegetated rural area: low complexity
    15. 15. Results: 2) Simulation methodology N-S, LBSS, LBSE the scalar value allows to set a desired value of the target residual energy on the orthogonal complement of the N-S estimated subspace <ul><li>simulation performed over N=1000 images </li></ul><ul><li>Target detection results averaged over the 1000 images </li></ul>
    16. 16. Results: 2) Simulation results (1000 images) Data-set
    17. 17. Results: 2) Simulation (1000 images), [email_address] D =1 LBSS: K L BSS is a user-specified parameter. No criteria exist to set it and several configurations have to be tested in order to assure good performance.
    18. 18. Results: 3) Testing on real data real target detection scenario with ground-truthed targets LBSE histogram ROC curves <ul><li>Best performance obtained with LBSE </li></ul><ul><li>LBSS results exhibit diversity w.r.t. K LBSS </li></ul>
    19. 19. Conclusion LBSE <ul><li>novel and fully automatic algorithm for local background subspace estimation and suppression </li></ul><ul><li>experimental evidence of three main advantages w.r.t exiting methodologies </li></ul>being local , it is able at properly detecting targets with low residual energy w.r.t the global background subspace provides unambiguous results through the automatic computation of a local background dimension for each pixel it is capable of adapting to spatial variations of background complexity within the scene
    20. 20. Pisa, 30.11.2007 Livorno, 30.04.2010 Thanks for your attention! South of Italy Chapter
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