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Discrimination of Basmati and Non-Basmati Rice Types Using Polarimetric Target Decomposition of Temporal Sar Data


Affiliations
1 Indian Institute Technology Bombay, Powai, Mumbai 400 076, India
2 Indian Institute of Remote Sensing (ISRO), 4, Kalidas Road, Dehradun 248 001, India
 

The present study distinguishes the growing areas of basmati and non-basmati rice types using polarimetric target decomposition technique on temporal Synthetic Aperture Radar (SAR) data. Multi-temporal quad-pol RADARSAT-2 data of part of the Indo-Gangetic plains were acquired to analyse the contribution of different scattering components (double bounce, singe bounce and volume scattering) at various crop growth stages of both rice types. A decision tree-based framework has been proposed to segregate both rice types and other major land use-land cover classes by capturing the temporal variations in different scattering components. Both rice types were separated in the study area with user's accuracy of 85.19% and 82.93% for non-basmati and basmati rice respectively.

Keywords

Basmati Rice, Decision-Tree Classifier, Polarimetric Target Decomposition, Synthetic Aperture Radar.
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  • Discrimination of Basmati and Non-Basmati Rice Types Using Polarimetric Target Decomposition of Temporal Sar Data

Abstract Views: 332  |  PDF Views: 134

Authors

Vineet Kumar
Indian Institute Technology Bombay, Powai, Mumbai 400 076, India
Mamta Kumari
Indian Institute of Remote Sensing (ISRO), 4, Kalidas Road, Dehradun 248 001, India
S. K. Saha
Indian Institute of Remote Sensing (ISRO), 4, Kalidas Road, Dehradun 248 001, India

Abstract


The present study distinguishes the growing areas of basmati and non-basmati rice types using polarimetric target decomposition technique on temporal Synthetic Aperture Radar (SAR) data. Multi-temporal quad-pol RADARSAT-2 data of part of the Indo-Gangetic plains were acquired to analyse the contribution of different scattering components (double bounce, singe bounce and volume scattering) at various crop growth stages of both rice types. A decision tree-based framework has been proposed to segregate both rice types and other major land use-land cover classes by capturing the temporal variations in different scattering components. Both rice types were separated in the study area with user's accuracy of 85.19% and 82.93% for non-basmati and basmati rice respectively.

Keywords


Basmati Rice, Decision-Tree Classifier, Polarimetric Target Decomposition, Synthetic Aperture Radar.



DOI: https://doi.org/10.18520/cs%2Fv110%2Fi11%2F2166-2169