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SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS

University POLITEHNICA Bucharest Facult y of Electronics, Telecommunication s and Information Technology. SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS. Anca Popescu, Inge Gavat University Politehnica Bucharest (UPB) Mihai Datcu

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SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS

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  1. University POLITEHNICA Bucharest Faculty of Electronics, Telecommunications and Information Technology SCENE CLASS RECOGNITION USINGHIGH 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

  2. Motivation: High resolution scene category indexing, large number of structures visible in urban sites Non – parametric feature extraction methods

  3. Summary • Introduction • Data descriptors • Spectral features • Spectral components • Experimental data • Classification results • Conclusions

  4. Introduction Study of value adding processing methods for SLC and InSAR data, for scene class recognition

  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. Data descriptors – spectral features Direct estimation from spectra based on spectral differences

  7. Data descriptors spectral components estimation Data model The 2-D signal model: Where complex amplitude of the kth sinusoid unknown frequencies of the kth sinusoid 2-D noise Problem: Estimate the parameters of the sinusoidal signals:

  8. Data descriptors spectral components estimation Model choice: minimize NLS criterion: Algorithm preparations: if αk and fk are known, minimize cost function for the kth sinusoid is: Peak of the 2-D periodogram Height of the peak (complex) J. Li, P. Stoica: “Efficient Mixed-Spectrum Estimation with Applications to Target Feature Extraction”, IEEE Transactions on Signal Processing, 44, 1996, 281-295

  9. Data descriptors spectral components estimation

  10. Data descriptors spectral components estimation 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 y = generated from f(x), X is a random variable Log likelihood function for model selection: Best Model: Minimum AIC value

  11. Methodology for scene class indexing Goal: asses parameter’s capability to discriminate scene classes Evaluation: Accuracy = (TP+TN) / (TP+TN+FP+FN)

  12. Test Site – Bucharest, Romania TerraSAR-X High Resolution Spotlight: LAN 130

  13. Test Site – Bucharest, Romania Experimental data – TSX LAN-130 Frequency of classes in database: dominant classes: tall blocks, green areas, urban fabric, commercial and industrial sites

  14. Test Site – Patch database formation 1650 SLC patches, 200 x 200 m Water course/ water body Stadion Tall Block Very tall building Industrial Site Interferometric data

  15. Results – Spectral and cepstral parameters Mean and variance of most significant 3 cepstral coefficients Spectral Centroid, Flux, and Rolloff (azimuth and range)

  16. Results – Spectral components Relax parameterαk(modulus representation), selection of six random components from the estimated stack Dense urban area, mostly tall blocks Green area, small vegetation

  17. Results – Spectral Components Reconstructed data from estimated spectral components and original SAR patch

  18. Results – Scene Class Recognition SLC/InSARTrue Negative Rate indicator 23 scene classes discoverable with SLC database 15 scene classes discoverable with InSAR database

  19. Results – Scene Class RecognitionSLC – Spectral Features and Spectral Components Accuracy indicator Spectral features Spectral components Class

  20. Results – Scene Class RecognitionSLC /InSAR SLC InSAR Influence of interferogram spectral features on classfication accuracy

  21. Conclusions • Evaluation of the capability of spectral parameters to discriminate scene classes for complex HR TerraSAR-X data • Spectral estimation method for high resolution data characterization and reconstruction, based on RELAX algorithm. • Evaluation of the capability of spectral componentsto discriminate scene classes for complex HR TerraSAR-X data • Accuracy of recognition better than 80% for main class training • Acknowledgements to DLR team for providing the interferometric data: Nico Adam, Christian Minet, Nestor Yague-Martinez , Helko Breit.

  22. Thank you for your attention! apopescu@lpsv.pub.ro

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