Machine learning (ML) methods and neural networks (NN) are widely implemented for crop types recognition and classification based on satellite images. However, most of these studies use several multi-temporal images which could be inapplicable for cloudy regions. We present a comparison between the classical ML approaches and U-Net NN for classifying crops with a single satellite image. The results show the advantages of using field-wise classification over pixel-wise approach. We first used a Bayesian aggregation for field-wise classification and improved on 1.5% results between majority voting aggregation. The best result for single satellite image crop classification is achieved for gradient boosting with an overall accuracy of 77.4% and macro F1-score 0.66.
@article{arxiv.2004.03468,
title = {Bayesian aggregation improves traditional single image crop classification approaches},
author = {Ivan Matvienko and Mikhail Gasanov and Anna Petrovskaia and Raghavendra Belur Jana and Maria Pukalchik and Ivan Oseledets},
journal= {arXiv preprint arXiv:2004.03468},
year = {2020}
}
Comments
Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)