A Minority Sample Enhanced Sampler for Crop Classification in Unmanned Aerial Vehicle Remote Sensing Images with Class Imbalance

Jiapei Cheng, Liang Huang (), Bohui Tang, Qiang Wu, Meiqi Wang and Zixuan Zhang
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Jiapei Cheng: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Liang Huang: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Bohui Tang: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Qiang Wu: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Meiqi Wang: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zixuan Zhang: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Agriculture, 2025, vol. 15, issue 4, 1-21

Abstract: Deep learning techniques have become the mainstream approach for fine-grained crop classification in unmanned aerial vehicle (UAV) remote sensing imagery. However, a significant challenge lies in the long-tailed distribution of crop samples. This imbalance causes neural networks to focus disproportionately on majority class features during training, leading to biased decision boundaries and weakening model performance. We designed a minority sample enhanced sampling (MES) method with the goal of addressing the performance limitations that are caused by class imbalance in many crop classification models. The main principle of MES is to relate the re-sampling probability of each class to the sample pixel frequency, thereby achieving intensive re-sampling of minority classes and balancing the training sample distribution. Meanwhile, during re-sampling, data augmentation is performed on the sampled images to improve the generalization. MES is simple to implement, is highly adaptable, and can serve as a general-purpose sampler for semantic segmentation tasks, functioning as a plug-and-play component within network models. To validate the applicability of MES, experiments were conducted on four classic semantic segmentation networks. The results showed that MES achieved mIoU improvements of +1.54%, +4.14%, +2.44%, and +7.08% on the Dali dataset and +2.36%, +0.86%, +4.26%, and +2.75% on the Barley Remote Sensing Dataset compared with the respective benchmark models. Additionally, our hyperparameter sensitivity analysis confirmed the stability and reliability of the method. MES mitigates the impact of class imbalance on network performance, which facilitates the practical application of deep learning in fine-grained crop classification.

Keywords: crop classification; deep learning; long-tailed distribution; precision agriculture; re-sampling; UAV remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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