Evaluation of different machine learning algorithms for pearl millet discrimination using multi-sensor SAR data

Abstract

This study aims to assess the potential of multi-temporal Sentinel-1 and RADARSAT-2 data for pearl millet discrimination using various classifiers—machine learning and knowledge-based decision tree (m-DT and k-DT), Support Vector Machine (SVM), and Random Forest (RF). Results show that the RF classifier outperformed the other classifiers in terms of overall accuracy (OA) and kappa coefficient. The highest Overall Accuracy (OA) of 89.1% and kappa coefficients of 0.822 and 0.814 were achieved by the RF classifier for both the Sentinel-1 and RADARSAT-2 data, respectively. The Variable Importance (VI) of the RF classifier revealed that the polarimetric parameters—volume scattering, double bounce, entropy and alpha angle were crucial for pearl millet discrimination. Polarimetric parameters, when ingested with the RF machine learning classifier, achieved better classification accuracies.

Keywords:

• Pearl millet
• crop discrimination
• polarimetric parameters
• Sentinel-1
• RADARSAT-2

Acknowledgements

The authors are extremely grateful to Dr Prakash Chauhan, Director, IIRS and Dr Sameer Saran, Head, Geoinformatics Department, IIRS, for constant encouragement and support. The authors are thankful to the SUFALAM project for providing Remote Sensing and field data. The authors are thankful to Abhinav Verma (Research Fellow, CSRE, IITB) for his support in ground truth data collection. The authors are also thankful to the reviewers for their valuable comments and suggestions, which is very important to improve the paper’s quality.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.