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Multi-Frequency Polarimetric SAR Data Analysis for Crop Type Classification Using Random Forest

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Synthetic Aperture Radar (SAR) Data Applications

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 199))

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Abstract

This chapter investigates multi-frequency (C-, L-, and P-bands) single-date AIRSAR data using Random Forest (RF) based polarimetric parameter selection for crop separation and classification. The RF classifier has an inherent parameter ranking and partial probability plot ability which gives not only the important parameters but also their optimal dynamic range. Crop separation was assessed among crop types by identifying polarimetric parameters having highest difference of Mean Decrease Accuracy (MDA) scores as measured by RF. Earlier studies primarily focused on polarimetric backscattering coefficients for crop analysis. In this study in addition to these parameters, the scattering decomposition powers along with the backscattering ratio parameters were also analyzed and found vital for multi-frequency crop classification. The Yamaguchi model-based decomposition, the Cloude-Pottier and the Touzi decomposition parameters provided complimentary information which were further used for critical analysis of crops in this study. In this study, the classification accuracy using RF was obtained as: C-band (71.9%); L-band (80.7%); P-band (75.8%). The long-stem crops: barley and rapeseed had the best accuracy in L-band (91.7%) and C-band (91.4%), respectively, while for the short-stem broad-leaf crops: sugarbeet (86.2%) in L-band and potatoes (95.4%) in L-band and (94.5%) in P-band, respectively.

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Notes

  1. 1.

    https://airsar.asf.alaska.edu/data/cm/cm3253/.

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Acknowledgements

The authors would like to thank the NASA/JPL for providing AIRSAR data products. Authors acknowledge the GEO-AWS Earth Observation Cloud Credits Program, which supported the computation on AWS cloud platform through the project: “AWS4AgriSAR-Crop inventory map** from SAR data on cloud computing platform.”

Author information

Authors and Affiliations

  1. Computer Engineering Department, TPCT’s Terna Engineering College, Mumbai, India

    Siddharth Hariharan

  2. Kansas State University, Manhattan, KS, USA

    Dipankar Mandal

  3. Geophysical and Multiphase Flows Laboratory, IDP in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India

    Siddhesh Tirodkar

  4. Department of Water Resources, Delft University of Technology, Delft, The Netherlands

    Vineet Kumar

  5. Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India

    Avik Bhattacharya

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  1. Siddharth Hariharan
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  2. Dipankar Mandal
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  3. Siddhesh Tirodkar
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  4. Vineet Kumar
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  5. Avik Bhattacharya
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Corresponding author

Correspondence to Dipankar Mandal .

Editor information

Editors and Affiliations

  1. Information Systems & Analytics, Miami University, Oxford, OH, USA

    Maciej Rysz

  2. Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Arsenios Tsokas

  3. U.S. Airforce Research Laboratory (AFRL), Eglin AFB, FL, USA

    Kathleen M. Dipple

  4. U.S. Airforce Research Laboratory (AFRL), Eglin AFB, FL, USA

    Kaitlin L. Fair

  5. Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Hariharan, S., Mandal, D., Tirodkar, S., Kumar, V., Bhattacharya, A. (2022). Multi-Frequency Polarimetric SAR Data Analysis for Crop Type Classification Using Random Forest. In: Rysz, M., Tsokas, A., Dipple, K.M., Fair, K.L., Pardalos, P.M. (eds) Synthetic Aperture Radar (SAR) Data Applications. Springer Optimization and Its Applications, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-031-21225-3_8

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