SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS  Anca Popescu, Inge Gavat Universit...
Motivation:  High resolution scene category indexing, large number of structures visible in urban sites Non – parametric f...
Summary <ul><li>Introduction </li></ul><ul><li>Data descriptors </li></ul><ul><ul><li>Spectral features </li></ul></ul><ul...
Introduction Study of value adding processing methods for SLC and InSAR data, for scene class recognition
Introduction Study of value adding processing methods for SLC and InSAR data, for scene class recognition  Concept:  Make ...
Data descriptors – spectral features Direct estimation from spectra based on spectral differences
Data model The 2-D signal model: Where complex amplitude of the k th  sinusoid unknown frequencies of the k th  sinusoid 2...
J. Li, P. Stoica: “Efficient Mixed-Spectrum Estimation with Applications to Target Feature Extraction”, IEEE Transactions ...
Data descriptors  spectral components estimation
AKAIKE Information Criterion  – model order selection Estimates the expected Kullback-Leibler information between the mode...
Goal: asses parameter’s capability to discriminate scene classes Evaluation:  Accuracy = (TP+TN) / (TP+TN+FP+FN) Methodolo...
Test Site – Bucharest, Romania TerraSAR-X High Resolution Spotlight: LAN 130
Frequency of classes in database: dominant classes: tall blocks, green areas, urban fabric, commercial and industrial site...
1650 SLC patches, 200 x 200 m Water course/ water body Stadion Very tall building Tall Block Industrial Site Test Site – P...
Spectral Centroid, Flux, and Rolloff (azimuth and range) Mean and variance of most significant 3 cepstral coefficients Res...
Relax parameter   α k   (modulus representation), selection of six random components from the estimated stack Results – Sp...
Reconstructed data from estimated spectral components and original SAR patch Results – Spectral Components
Results – Scene Class Recognition SLC/InSAR True Negative Rate indicator 23 scene classes discoverable with SLC database 1...
Results – Scene Class Recognition SLC – Spectral Features and Spectral Components Accuracy indicator Class Spectral compon...
Influence of interferogram spectral features on classfication accuracy Results – Scene Class Recognition SLC /   InSAR InS...
<ul><li>Evaluation  of  the capability of  spectral parameters  to discriminate scene classes  for complex HR  TerraSAR-X ...
Thank you for your attention! [email_address]
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  • Popescu.ppt

    1. 1. SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS Anca Popescu, Inge Gavat University Politehnica Bucharest (UPB) Mihai Datcu German Aerospace Center (DLR) IGARSS 2011 24-29 July 2011, Vancouver, Canada University POLITEHNICA Bucharest Facult y of Electronics, Telecommunication s and Information Technology
    2. 2. Motivation: High resolution scene category indexing, large number of structures visible in urban sites Non – parametric feature extraction methods
    3. 3. Summary <ul><li>Introduction </li></ul><ul><li>Data descriptors </li></ul><ul><ul><li>Spectral features </li></ul></ul><ul><ul><li>Spectral components </li></ul></ul><ul><li>Experimental data </li></ul><ul><li>Classification results </li></ul><ul><li>Conclusions </li></ul>
    4. 4. Introduction Study of value adding processing methods for SLC and InSAR data, for scene class recognition
    5. 5. Introduction Study of value adding processing methods for SLC and InSAR data, for scene class recognition Concept: Make use of the information contained in the phase of the SAR signal
    6. 6. Data descriptors – spectral features Direct estimation from spectra based on spectral differences
    7. 7. Data model The 2-D signal model: Where complex amplitude of the k th sinusoid unknown frequencies of the k th sinusoid 2-D noise Data descriptors spectral components estimation Problem: Estimate the parameters of the sinusoidal signals:
    8. 8. J. Li, P. Stoica: “Efficient Mixed-Spectrum Estimation with Applications to Target Feature Extraction”, IEEE Transactions on Signal Processing, 44, 1996, 281-295 Model choice: minimize NLS criterion: Peak of the 2-D periodogram Algorithm preparations: if α k and f k are known, minimize cost function for the k th sinusoid is: Height of the peak (complex) Data descriptors spectral components estimation
    9. 9. Data descriptors spectral components estimation
    10. 10. AKAIKE Information Criterion – model order selection Estimates the expected Kullback-Leibler information between the model generating the data and a candidate model Model selection: = parameter to be estimated from empirical data y Best Model: Minimum AIC value y = generated from f(x), X is a random variable Log likelihood function for model selection: Data descriptors spectral components estimation
    11. 11. Goal: asses parameter’s capability to discriminate scene classes Evaluation: Accuracy = (TP+TN) / (TP+TN+FP+FN) Methodology for scene class indexing
    12. 12. Test Site – Bucharest, Romania TerraSAR-X High Resolution Spotlight: LAN 130
    13. 13. Frequency of classes in database: dominant classes: tall blocks, green areas, urban fabric, commercial and industrial sites Experimental data – TSX LAN-130 Test Site – Bucharest, Romania
    14. 14. 1650 SLC patches, 200 x 200 m Water course/ water body Stadion Very tall building Tall Block Industrial Site Test Site – Patch database formation Interferometric data
    15. 15. Spectral Centroid, Flux, and Rolloff (azimuth and range) Mean and variance of most significant 3 cepstral coefficients Results – Spectral and cepstral parameters
    16. 16. Relax parameter α k (modulus representation), selection of six random components from the estimated stack Results – Spectral components Dense urban area, mostly tall blocks Green area, small vegetation
    17. 17. Reconstructed data from estimated spectral components and original SAR patch Results – Spectral Components
    18. 18. Results – Scene Class Recognition SLC/InSAR True Negative Rate indicator 23 scene classes discoverable with SLC database 15 scene classes discoverable with InSAR database
    19. 19. Results – Scene Class Recognition SLC – Spectral Features and Spectral Components Accuracy indicator Class Spectral components Spectral features
    20. 20. Influence of interferogram spectral features on classfication accuracy Results – Scene Class Recognition SLC / InSAR InSAR SLC
    21. 21. <ul><li>Evaluation of the capability of spectral parameters to discriminate scene classes for complex HR TerraSAR-X data </li></ul><ul><li>Spectral estimation method for high resolution data characterization and reconstruction, based on RELAX algorithm. </li></ul><ul><li>Evaluation of the capability of spectral components to discriminate scene classes for complex HR TerraSAR-X data </li></ul><ul><li>Accuracy of recognition better than 80% for main class training </li></ul><ul><li>Acknowledgements to DLR team for providing the interferometric data: Nico Adam, Christian Minet, Nestor Yague-Martinez , Helko Breit. </li></ul>Conclusions
    22. 22. Thank you for your attention! [email_address]

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