Machine learning-based meta-classifier for Kharif Bajra (pearl millet) discrimination in the mixed cropping environment using multi-temporal SAR data

Abstract

Study on pearl millet discrimination in mixed cropping environment was carried out as it forms the major coarse cereal crop in India. The study aims at determining the capabilities of Sentinel-1A SAR data and Machine Learning (ML) algorithms for pearl millet discrimination. Six ML algorithms, that is, k-nearest neighbour (k-NN), Decision Tree (DT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP) and Naïve Bayes (NB) were evaluated and compared. Meta-classifier using majority voting technique was proposed by ensembling classifiers (RF, SVM and NB) which were diverse and have comparable accuracies. The diversity assessment was conducted using Cochran Q statistics for ensembling purpose. RF classifier (83.76%) using VH polarization produced better result indicating importance of cross-polarization in crop classification. Further, meta-classifier proposed in the study improved the accuracy to 88.15% (approximately by 5%) and class accuracies of pearl millet resulting in improved discrimination of pearl millet in mixed cropping environment.

Keywords:

• Sentinel-1A
• multi-temporal SAR
• crop discrimination
• machine learning algorithms
• meta-classifier
• crop phenology

Acknowledgement

The author would like to thank reviewer for providing valuable feedback in improving the manuscript.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Indian Space Research Organization.