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

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.