Artificial neural network for crop classification using C-band RISAT-1 satellite datasets

Abstract

The artificial neural network algorithm has been used for the classification of rice, corn, pigeon pea, green gram, other crop and non-crop classes in Varanasi District, Uttar Pradesh, India. The C-band, dual polarimetric, temporal satellite datasets of RISAT-1 have been carried out in the present study. The separability analysis using transformed divergence and Jefferies Matusita distance methods were compared. The transformed divergence method has shown better separation between the classes in comparison to Jefferies Matusita distance method. The classification results were ground validated. The overall achieved accuracies were 74.21 and 77.36% for satellite data acquired on 9 August 2013 and 28 September 2013 respectively. Results have shown the better classification accuracy using 28th September 2013 data because of almost crops were in the reproductive stage and high reflectivity of the crops at this stage.