Sar polarimetric data analysis for identification of ships

1. Sar polarimetric data analysis for identification of ships India Geospatial Forum – 14th International Conference February 07-09, 2012, Gurgaon S. Swarajya lakshmi ADRIN, Dept. of Space, Govt. of India

2. Objectives • Exploitation of polarimetric SAR data for detection of ships • Understanding the scattering mechanisms of ships through decomposition • Feasibility for deriving additional information for identification and classification of ships

3. Polarization Combinations VV HH HV VH pq – p –transmit q -receive

4. Polarimetry : Information Content • As compared to single-polarization SAR, polarimetric SAR provides additional information on: • Type of scatterer: Trihedral, dihedral, dipole etc. • Orientationof the scatterer about the radar line of sight • Ellipticity:degree of scatterer symmetry • Entropy: significance of the polarimetric information • Therefore, enables better characterization of the target

5. Scattering Mechanisms T11= |hh+vv|2 T22 = |hh-vv|2T33=2|hv|2

6. Polarimetric Signature Horizontal Polarization  = 0º or 180º Vertical Polarization  = 90º Pedestal Height Circular Polarization Elliptical Polarization Linear Polarization Vertical Polarization Linear Polarization  = 0º Elliptical Polarization -45º <  < 0º and 0º <  < +45º Circular Polarization  = -45º or +45º

7. Materials & Methods Data Used: Software Used: POLSAR of ESA

8. Methodology

9. Steps Involved • Input SLC data • Sinclair Matrix – Shh, Shv, Svh, Svv • Extracting Different Target Descriptors – • Stokes matrix, Covariance Matrix, Cherence Matrix • Speckle Filtering • Polarimetric Parameter Extraction – • Total Power, Entropy, Alpha, Anisotropy, Degree of Polarisation, Eigen Analysis parameters etc. • Extracting Polarimetric signatures • Polarimetric Synthesis • Polarimetric Decomposition and Classification • Separation of Land and Water • Identification of anomalies in water • Identification of ships • Further characterisation of ships with respect to polarimetric parameters

10. Entropy • Eigen Values: Three eigen values of the 3x3 Coherency matrix λi represent the intensities of the three main scattering mechanisms • Probabilities Pi of each scattering mechanism • Entropy (H) • This is a measure of the dominance of a given scattering mechanism within a resolution cell. • Entropy ranging from 0 to 1, represents the randomness of a scattering medium • from isotropic scattering (H=0) • to totally random scattering (H=1) Where, ENTROPY

11. = first element of the ith eigenvector Alpha If the Entropy is close to 0, the alpha angle provides the nature or type of the dominant scattering mechanism for that resolution cell. For example it will identify if the scattering is volume, surface or double bounce. anisotropic odd bounce anisotropic even bounce  = 90  = 45  = 0 Isotropic odd bounce Isotropic even bounce Multiple ALPHA

12. Anisotropy (A) This is the measure of how homogeneous a target is relative to the radar look direction. For example, the Amazon forest is a very homogeneous target and would have a low anisotropy value. In contrast, row crops would have a high anisotropy value. A indicates the distribution of the two less significant eigenvalues Anisotropy becomes 0 if both scattering mechanisms are of an equal proportion; values of A > 0 indicates increasing amount of anisotropic scattering.

13. Target Decomposition • Analysis methods whereby individual scattering components that have meaningful physical interpretation can be identified in the received signal. • Scattering matrix is decomposed into sub-matrices so that Individual component have physical meaning => Surface scatterer, double bounce, volume scattering

14. H-Alpha Scattering Plane

15. Classified image depicting water and ships Classified Image Anomalies in water identified as ships Land Ships with typical signatures

16. Class description

17. Scattering Mechanisms with respect to the ships identified Turbulence of water Boundary between water & metallic ship body Ship structure Objects causing strong double bounce scattering

18. Polarimetric signatures

19. Polarimetric signatures – ship

20. Proportion (%) of pixels for each class of scattering

21. Proportion (%) of pixels for each class of scattering

22. Derived Information on Ship Measurements

23. Conclusions &Way Forward • Typical scattering mechanisms were observed to be associated with the ships, which could be used towards automated detection and characterization of ships. • Potential of the polarimetric data cold be further explored with multi-parametric decomposition schemes and tested with a wide variety of ships.

24. THANKS FOR YOUR KIND ATTENTION