Impact of Training Data Quality on Machine Learning Based Crop Classification Using Time Series C-Band SAR Data

Supervised classification of time series image classification through state-of-the-art Machine Learning algorithms such as Random Forest demands good quality training data for achieving good classification accuracy. A pilot study has been carried out to find out the effect of training data quality on discriminability of kharif maize crop from competing crops using multi-date C-band Synthetic Aperture Radar (SAR) data. The study was done in five districts of Telangana and four districts each of Madhya Pradesh and Maharashtra. Fairly good discrimination of kharif maize using C-band SAR with average classification accuracy of 85% have been obtained where at least 30 good quality ground truth (GT) polygons and similar number of GT for competing crops were available.