Crop classification from C-band polarimetric radar data

Synthetic aperture radar "SAR" systems are an attractive source of information for agricultural crop classification applications, particularly in regions where cloud cover is a problem. The accuracy with which crops can be classified is dependent on a range of sensor properties, including the SAR operating configuration. This paper focuses on the effect of one aspect of the SAR operating configuration, polarization, on crop classification accuracy using uncalibrated C-band polarimetric SAR data. Conventional like- and cro spolarized configurations and polarimetric coefficients "the pedestal and variation coefficients" were used as discriminating variables in classifications of agricultural crops. Two approaches to classification were investigated, a discriminant analysis and an artif cial neural network and results from a set of training classifications are presented. The results show that the polarimetric coefficients used provided a high level of inter-class discrimination and that a nine-class classification with an accuracy of up to 78·75 per cent could be produced from these C-band polarimetric SAR data. Classification accuracy was also influenced by the classification technique, with the neural network achieving significantly higher levels of inter-class separability in the training data than the discriminant analysis.