polsartools 0.6

A python package for processing Polarimetric Synthetic Aperture Radar (PolSAR) data.

PolSARtools PyPI package

Cite: 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

@article{bhogapurapu2021polsar,
  title={PolSAR tools: A QGIS plugin for generating SAR descriptors},
  author={Bhogapurapu, Narayanarao and Dey, Subhadip and Mandal, Dipankar and Bhattacharya, Avik and Rao, YS},
  journal={Journal of Open Source Software},
  volume={6},
  number={60},
  pages={2970},
  year={2021},
  doi= {10.21105/joss.02970}
}

General Information

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This package generates derived SAR parameters (viz. vegetation indices, polarimetric decomposition parameters) from input polarimetric matrix (C3, T3, C2, T2). The input data needs to be in PolSARpro/ENVI format (*.bin and *.hdr).

Installation

pip install polsartools

Prerequesites

gdal, Numpy

gdal installation error fix

conda install gdal

Example

import polsartools as pst

T3_folder = r'../T3'
windows_size=3

pst.mf4cf(T3_folder,window_size=window_size)

ps,pd,pv,pc,tfp,taufp = pst.mf4cf(T3_folder,window_size=window_size,write_flag=False)

#%%
compact_c2 = r'./sample_data/compact_pol/C2_RHV'

cprvi = pst.cprvi(compact_c2,chi_in=45,window_size=3,  write_flag=False)
dcp = pst.dopcp(compact_c2,chi_in=45,window_size=3,  write_flag=False)
ccp = pst.mf3cc(compact_c2,chi_in=45,window_size=3,  write_flag=False)
socp = pst.misomega(compact_c2,chi_in=45,psi_in=0,window_size=3,  write_flag=False)
print('compact pol')
#%%
full_T3 = r'./sample_data/full_pol/T3'

mf3 = pst.mf3cf(full_T3,window_size=3,write_flag=False)
mf4 = pst.mf4cf(full_T3,window_size=3,write_flag=False)
dfp = pst.dopfp(full_T3,window_size=3,write_flag=False)
grvi = pst.grvi(full_T3,window_size=3,write_flag=False)
rvi = pst.rvifp(full_T3,window_size=3,write_flag=False)
prvi = pst.prvifp(full_T3,window_size=3,write_flag=False)

print('full pol')

#%%

dxp_C2 = r'./sample_data/dual_pol/C2_VVVH'

dpr = pst.dprvi(dxp_C2,window_size=3,write_flag=False)
rvdp = pst.rvidp(dxp_C2,window_size=3,write_flag=False)
prvdp = pst.prvidp(dxp_C2,window_size=3,write_flag=False)
ddp = pst.dopdp(dxp_C2,window_size=3,write_flag=False)

print('dual cross-pol')

sample use case is provided in tests

Available functionalities:

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• Full-pol :

  • Model free 4-Component decomposition for full-pol data (MF4CF)[11]
  • Model free 3-Component decomposition for full-pol data (MF3CF)[4]
  • Radar Vegetation Index (RVI) [8]
  • Generalized volume Radar Vegetation Index (GRVI) [2]
  • Polarimetric Radar Vegetation Index (PRVI) [1]
  • Degree of Polarization (DOP) [10]

• Compact-pol :

  • Model free 3-Component decomposition for compact-pol data (MF3CC) [4]
  • Improved S-Omega decomposition for compact-pol data (iS-Omega) [7]
  • Compact-pol Radar Vegetation Index (CpRVI) [6]
  • Degree of Polarization (DOP) [10]

• Dual-pol:

  • Dual-pol Radar Vegetation Index (DpRVI) [5]
  • Dual-pol Radar Vegetation Index for GRD data (DpRVIc) [12]
    • Radar Vegetation Index (RVI) [9]
  • Degree of Polarization (DOP) [10]
  • Polarimetric Radar Vegetation Index (PRVI) [1]
  • Dual-pol descriptors [13]

Contributions

1. Contribute to the software

   Contribution guidelines for this project

2. Report issues or problems with the software

   Please raise your issues here : https://github.com/Narayana-Rao/polsartools/issues

3. Seek support

   Please write to us: [e-mail]

References

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[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] Kim, Y. and van Zyl, J.J., 2009. A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Transactions on Geoscience and Remote Sensing, 47(8), pp.2519-2527.

[9] Trudel, M., Charbonneau, F. and Leconte, R., 2012. Using RADARSAT-2 polarimetric and ENVISAT-ASAR dual-polarization data for estimating soil moisture over agricultural fields. Canadian Journal of Remote Sensing, 38(4), pp.514-527.

[10] Barakat, R., 1977. Degree of polarization and the principal idempotents of the coherency matrix. Optics Communications, 23(2), pp.147-150.

[11] 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. doi: 10.1109/JSTARS.2021.3069299.

[12] Bhogapurapu, N., Dey, S., Mandal, D., Bhattacharya, A., Karthikeyan, L., McNairn, H. and Rao, Y.S., 2022. Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data. Remote Sensing of Environment, 271, p.112900.

[13]Bhogapurapu, N., Dey, S., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J.M., McNairn, H., López-Martínez, C. and Rao, Y.S., 2021. Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 178, pp.20-35.