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2021 | OriginalPaper | Chapter

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

Authors : Shyamal Virnodkar, V. K. Pachghare, V. C. Patil, Sunil Kumar Jha

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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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.

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Metadata
Title
Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series
Authors
Shyamal Virnodkar
V. K. Pachghare
V. C. Patil
Sunil Kumar Jha
Copyright Year
2021
Publisher
Springer Singapore
Book
Progress in Advanced Computing and Intelligent Engineering

Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6353-9_15