A python based QGIS plugin

This plugin generates polarimetric descriptors (viz. vegetation indices, polarimetric decomposition parameters) from C3/T3/C2/T2 matrices obtained from PolSARpro The input data needs to be in PolSARpro/ENVI format (*.bin and *.hdr). It requires numpy, matplotlib python libraries pre-installed.

QGIS Python Plugin Repository: https://plugins.qgis.org/plugins/polsar_tools/

Or access from QGIS Desktop>Plugin Manager>Search for ‘PolSAR tools’

Available functionalities:

Indices

  • Radar Vegetation Index (RVI) (Full-pol and dual-pol)
  • Generalized volume Radar Vegetation Index (GRVI)
  • Polarimetric Radar Vegetation Index (PRVI) (Full-pol and dual-pol)
  • Dual-pol Radar Vegetation Index (DpRVI)
  • Degree of Polarization (DOP) (Full-pol, dual-pol, and compact-pol)
  • Compact-pol Radar Vegetation Index (CpRVI)

Decompositions:

  • Model free 3-Component decomposition for full-pol data (MF3CF).
  • Model free 4-Component decomposition for full-pol data (MF3CF).
  • Model free 3-Component decomposition for compact-pol data (MF3CC)
  • Improved S-Omega decomposition for compact-pol data (iS-Omega)
PolSAR tools plugin interface

Contributors:

Narayanarao Bhogapurapu, Subhadip Dey,
Dr. Dipankar Mandal, Dr. Avik Bhattacharya, Dr. Y. S. Rao
E-mail: mrscsre@gmail.com

Bhogapurapu, N., Dey, S., Mandal, D., Bhattacharya, A. and Rao, Y.S., 2021. PolSAR tools: A QGIS plugin for generating SAR descriptors. Journal of Open Source Software, 6(60), p.2970. doi: 10.21105/joss.02970

References:

Chang, J.G., Shoshany, M. and Oh, Y., 2018. Polarimetric Radar Vegetation Index for Biomass Estimation in Desert Fringe Ecosystems. IEEE Transactions on Geoscience and Remote Sensing, 56(12), pp.7102-7108.

Ratha, D., Mandal, D., Kumar, V., McNairn, H., Bhattacharya, A. and Frery, A.C., 2019. A generalized volume scattering model-based vegetation index from polarimetric SAR data. IEEE Geoscience and Remote Sensing Letters, 16(11), pp.1791-1795.

Mandal, D., Kumar, V., Ratha, D., J. M. Lopez-Sanchez, A. Bhattacharya, H. McNairn, Y. S. Rao, and K. V. Ramana, 2020. Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data, Remote Sensing of Environment, 237: 111561.

Dey, S., Bhattacharya, A., Ratha, D., Mandal, D. and Frery, A.C., 2020. Target Characterization and Scattering Power Decomposition for Full and Compact Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing.

Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J.M., McNairn, H. and Rao, Y.S., 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, 247, p.111954.

Mandal, D., Ratha, D., Bhattacharya, A., Kumar, V., McNairn, H., Rao, Y.S. and Frery, A.C., 2020. A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 58 (9), pp. 6321-6335.

V. Kumar, D. Mandal, A. Bhattacharya, and Y. S. Rao, 2020. Crop Characterization Using an Improved Scattering Power Decomposition Technique for Compact Polarimetric SAR Data. International Journal of Applied Earth Observations and Geoinformation, 88: 102052.

S. Dey, A. Bhattacharya, A. C. Frery, C. Lopez-Martinez and Y. S. Rao, “A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.