Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series

Abstract

Sentinel-2 optical time-series images obtained at high resolution are creditable for cropland mapping which is the key for sustainable agriculture. The presented work was conducted in a heterogeneous region in Sameerwadi with an aim to classify sugarcane crops, with mainly two groups so as to provide a sugarcane field map, using Sentinel-2 normalized difference vegetation index (NDVI) time-series data. The potential of two better-known machine learning (ML) classifiers, random forest (RF) and support vector machine (SVM), was investigated to identify seven classes including sugarcane, early sugarcane, maize, waterbody, fallow land, built-up and bare land, and a sugarcane crop map is produced. Both the classifiers were able to effectively classify sugarcane areas and other land covers from the time-series data. Our results show that RF achieved higher overall accuracy (88.61%) than SVM having an overall accuracy of 81.86%. This study demonstrated that utilizing the Sentinel-2 NDVI time-series with RF and SVM successfully classified sugarcane crop fields.