PSVANZYL

Description

PSVANZYL performs an unsupervised van Zyl classification of a fully polarimetric SAR (POLSAR) data set. It classifies the image pixels into the most dominant of three scattering types: surface, dihedral, and volume.

Parameters

psvanzyl(fili, filo)

Name	Type	Caption	Length	Value range
FILI*	str	Input polarimetric SAR image	1 -
FILO*	str	Output van Zyl classified raster	1 -

* Required parameter

Parameter descriptions

FILI

Specifies the name of the input polarimetric SAR data set, which must be either non-symmetrized or symmetrized fully polarimetric (quad-polarization) complex data . The input data set must be in covariance, coherence, or Kennaugh matrix format. The input data set should have an equivalent number of looks (ENL) of at least 25. If the data set is single-look complex, an ENL of 25 could be achieved by applying a polarimetric filter (such as boxcar).

FILI must be a data set that is already imported into the PCIDSK format by SARINGEST. For more information and a complete list of supported POLSAR sensors and data products, see the SARINGEST Help.

FILO

Specifies the name of the output classified raster. The output file has the same dimensions as the input, and one channel. The output channel is 8-bit, where each pixel value represents the assigned van Zyl class number (0 to 3). The non-classified pixels are assigned to class 0.

The specified file must not already exist.

Details

PSVANZYL performs an unsupervised vanZyl classification of a polarimetric SAR data set. The classification assigns pixels to one of three types of scatterers: an odd (surface), even (dihedral), and diffuse (volume) scattering.

The input polarimetric SAR data set must represent a non-symmetrized or symmetrized fully polarimetric (quad-polarization, complex) data set in the covariance (c4r6c or C3r3c), coherency (t4r6c or T3r3c), or Kennaugh (k16r or K9r) matrix format. For analysis the input matrix is internally converted to the symmetrized covariance format, C3r3c.

The unsupervised van Zyl and Freeman-Durden classifications are similar. The main difference is that while PSVANZYL finds the single dominant scatterer type for each pixel, the Freeman-Durden classifications partitions the total scattered power into contributions from the three scattering mechanisms and returns the contribution from each scattering mechanism to the total power of each pixel.

Example

Perform an unsupervised van Zyl classification of a SIR_C data set. The input SIR-C MLC-Q data set is already imported into the PCIDSK format.

from pci.psvanzyl import *

fili	=	"sir_c.pix"
filo	=	"sc_vanZyl.pix"
psvanzyl( fili, filo )
            

Algorithm

The algorithm in PSVANZYL is based on the article in the References section, below. It classifies pixels based on simple rules involving elements of the symmetrized covariance matrix C3r3c. The assigned class numbers correspond to the following scattering mechanisms:

1. pixel value 0 - Non-classifiable
2. pixel value 1 - Odd number of reflections (surface scatterer)
3. pixel value 2 - Even number of reflections (dihedral scatterer)
4. pixel value 3 - Diffuse scattering (volume scatterer)

The following elements, Pij, of the symmetrized covariance matrix C3r3c are used in the algorithm:

P11 = |Shh|^2
P22 = |Shv|^2
P33 = |Svv|^2
P13 = Re((Shh*) * Svv)
            

Sij represents the elements of the scattering matrix, Re() is the real part of a complex number, and the asterisk (*) represents the complex conjugate of a number.

The processing in PSVANZYL proceeds as follows.

• If required, convert the input data set to the symmetrized covariance matrix format. For details about the conversion process, see the PSCONV Help.
• Loop over all image pixels. At each pixel:
  • Extract the four elements of the covariance matrix, P11, P22, P33, and P13.
  • Assign each pixel to the one of the classes as follows:

    IF P11 > P22 AND P33 > P22 THEN
      IF |P13| > P22 THEN
        IF P13 > 0 THEN
          CLASS = 1
        ELSE
          CLASS = 2
        ENDIF
      ELSE
        CLASS = 3
      ENDIF
    ELSE
      CLASS = 4
    ENDIF
                                    

  • Store the class number in the current pixel of the output channel.

Acknowledgements

PCI Geomatics gratefully acknowledges the financial support provided by the Canadian Space Agency through the Earth Observation Application Development Program (EOADP), contract number 9F028-034946.

References

van Zyl, J.J. "Unsupervised classification of scattering mechanisms using radar polarimetry data", IEEE Trans. Geosci. Remote Sensing, 27, no. 1 (1989): 36-45.