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Fully vs dual polarization satellite sensors for urban area analysis

POLinSAR 2005 Frascati, January 17–21 2005. Fully vs dual polarization satellite sensors for urban area analysis. F. Dell’Acqua, P. Gamba, G. Trianni University of Pavia. Presenter: Giovanna Trianni. Purpose of the study.

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Fully vs dual polarization satellite sensors for urban area analysis

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  1. POLinSAR 2005Frascati, January 17–21 2005 Fully vs dual polarization satellite sensors for urban area analysis F. Dell’Acqua, P. Gamba, G. Trianni University of Pavia Presenter:Giovanna Trianni

  2. Purpose of the study • Exploration of the use of dual and fully polarimetric SAR data for urban area characterization. • Discrimination among some important land cover classes (built up areas, vegetation, water) . • Discrimination among different urban environments (city center vs residential areas vs sparse buildings). • Investigation of the role of the phase data

  3. Available data • A fully polarized SIR-C image (H/H, H/V, V/H and V/V bands) • A dual polarized SIR-C image (H/H and H/V bands) • An alternating polarization ASAR image (H/H and V/V polarizations) • An image mode ASAR data (V/V polarization) The data set used in this study is composed by some takes of the city of Pavia, northern Italy, acquired by different sensors:

  4. V/V polarization of 14th April 1994 H/V polarization of 14th April 1994 H/H polarization of 14th April 1994 Sir-C fully polarized data

  5. H/V polarization of 17th April 1994 H/H polarization of 17th April 1994 Sir-C dual polarized data

  6. Image mode data of 25th November 2002 V/V polarization AP data of 29th August 2003 V/V polarization AP data of 29th August 2003 H/H polarization ASAR data

  7. Image segmentation on a pixel-by-pixel basis Discrimination of land cover classes 4-POL  2-POL  No-POL  2-POL 

  8. Discrimination of urban land cover classes • The objects in an urban area are very different one from the other; • The use of a single scale reduces the quality of the classification map at the border among different zones. Problems Solution We propose a new methodology to perform the spatial analysis and obtain the optimal scale for each pixel.

  9. Methodology Definition of the maximum spatial scale in the image through a global scale search Refining of the scale analysis through a local scale search, looking only at the local neighborhood of a pixel

  10. Co-occurrence measures • Eight textural measures • Four textures are enough • Histogram Distance Index After having defined the optimal scale for each pixel: best set of four textures

  11. Some results for the fully polarimetric data We classified the four textures for each polarization and all of the textures together.

  12.  = SHH - SHV = - SHV  = SHH - SVV = - SVV = SHV - SVV =  -  Role of the phase data • The available Sir-C data were stored in MLC (compressed Multi Look Complex) format; • This format causes the loss of information about one of the phases; • Under the hypothesis that HH polarization’s phase is null, we can write the polarimetric covariance matrix as:

  13. Conclusions • Single polarization data sets from ASAR sensor provide results similar to those from one of the polarization bands of the SIR-C sensor. • ASAR instrument provides the same classification performances as the SIR-C SAR sensor. • Texture measures allow discrimination of different built-up classes to some extent. • Fully polarimetric data are not strictly necessary, since a similar accuracy can be obtained from dual polarization data. • Phase data seem to add no relevant information for the considered purpose.

  14. In the future The role of more complex polarimetric decompositions at this coarse resolution.

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