DirPA: Addressing Prior Shift in Imbalanced Few-shot Crop-type Classification

2026-03-13

AI Summary

This work addresses the prior shift problem in agricultural remote sensing crop classification, which arises from the mismatch between the true long-tailed class distribution in real-world settings and the artificially balanced training sets commonly used. To this end, the study introduces Dirichlet Prior Augmentation (DirPA) into cross-regional agricultural scenarios for the first time, integrating it within a few-shot learning framework to actively simulate the authentic class prior distribution. Experiments across multiple European Union countries demonstrate that the proposed approach effectively mitigates distributional shift, significantly enhancing model training stability, generalization capability, and per-class classification accuracy under extreme long-tailed and label-scarce conditions. These results confirm the method’s adaptability and robustness across diverse geographical environments.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Remote Sensing / Geospatial AI
• Multiagent Systems: Distributed Problem Solving

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Real-world agricultural monitoring is often hampered by severe class imbalance and high label acquisition costs, resulting in significant data scarcity. In few-shot learning (FSL) -- a framework specifically designed for data-scarce settings -- , training sets are often artificially balanced. However, this creates a disconnect from the long-tailed distributions observed in nature, leading to a distribution shift that undermines the model's ability to generalize to real-world agricultural tasks. We previously introduced Dirichlet Prior Augmentation (DirPA; Reuss et al., 2026a) to proactively mitigate the effects of such label distribution skews during model training. In this work, we extend the original study's geographical scope. Specifically, we evaluate this extended approach across multiple countries in the European Union (EU), moving beyond localized experiments to test the method's resilience across diverse agricultural environments. Our results demonstrate the effectiveness of DirPA across different geographical regions. We show that DirPA not only improves system robustness and stabilizes training under extreme long-tailed distributions, regardless of the target region, but also substantially improves individual class-specific performance by proactively simulating priors.

Problem

• prior shift
• class imbalance
• few-shot learning
• long-tailed distribution
• crop-type classification

Innovation

• Dirichlet Prior Augmentation
• few-shot learning
• class imbalance
• prior shift
• crop-type classification