Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification

• URL: http://arxiv.org/abs/2511.16218v1
• Date: Thu, 20 Nov 2025 10:39:25 GMT
• Title: Mind the Gap: Bridging Prior Shift in Realistic Few-Shot Crop-Type Classification
• Authors: Joana Reuss, Ekaterina Gikalo, Marco Körner,
• Abstract summary: Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution.<n>We propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training.<n>Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer.
• Score: 0.3823356975862005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced -- in particular in the case of few-shot learning -- failing to reflect real-world conditions. This mismatch induces a shift between training and test label distributions, degrading real-world generalization. To address this, we propose Dirichlet Prior Augmentation (DirPA), a novel method that simulates an unknown label distribution skew of the target domain proactively during model training. Specifically, we model the real-world distribution as Dirichlet-distributed random variables, effectively performing a prior augmentation during few-shot learning. Our experiments show that DirPA successfully shifts the decision boundary and stabilizes the training process by acting as a dynamic feature regularizer.

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