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

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 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. 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. Data descriptors spectral components estimation

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

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

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

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

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. Results – Scene Class Recognition SLC – Spectral Features and Spectral Components Accuracy indicator Class Spectral components Spectral features

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

22. Thank you for your attention! [email_address]