## 1 Introduction  
Farmland classification, including crop classification, is essential for agricultural monitoring, yield estimation, and policy-making. Advances in satellite-based remote sensing have enabled accurate mapping of farmland distribution and characteristics over extensive areas, greatly enhancing resource management and decision-making [1]. Early remote sensing-based farmland classification mainly employed pixel-wise classification methods [2]. However, these methods have notable drawbacks, including \"salt-and-pepper\" noise resulting from isolated misclassified pixels and difficulty handling mixed pixels [3]. To overcome these limitations, Object-Based Image Analysis (OBIA) emerged as an alternative. OBIA segments satellite images into meaningful objects (e.g., parcels) and classifies these based on statistical features such as the mean Normalized Difference Vegetation Index (NDVI), texture, and area, typically using traditional machine learning algorithms [4, 5]. Recently, convolutional neural network (CNN)-based deep learning techniques have increasingly been applied to farmland classification. These approaches can be broadly categorized into two types: (i) methods that first extract field boundaries and subsequently classify each parcel using CNNs [6; 7; 8; 9] and (ii) end-to-end segmentation models that directly learn from pixel-level data to predict land cover classes for entire images [10; 11; 12]. In parallel, research focusing on precise field-boundary extraction has gained traction [13; 14; 15; 16], aiming to improve object definitions within classification pipelines. Alongside these technical advances, numerous studies have reported that not only the design of model architectures but also non-architectural factors--such as the composition of training samples, data sampling methods, and pretraining strategies--can play a significant role in improving classification performance [17; 18; 19; 20]. Based on this perspective, the present study aims to improve performance by refining the method of training for data extraction while preserving the model architecture. Farmland classification is also particularly sensitive to temporal and regional variability. Even identical crop types can exhibit significant spectral and textural differences, driven by variations in growth stages, soil properties, and climate conditions across regions [21]. These factors critically impact the generalization ability of classification models, emphasizing the need for methods that maintain robust performance across spatio-temporal variations. To address this need, the present study aims to develop a farmland classification model capable of strong generalization--both spatially and temporally. Class imbalance is among the most significant challenges in farmland classification. Due to variations in crop cultivation areas influenced by region, season, and agricultural policies, farmland datasets often exhibit highly imbalanced class distributions. This trend has also been observed in previous studies, where farmland data were found to be heavily skewed toward a few dominant crop types, while rare crops appeared only sparsely [22]. For instance, staple crops such as rice and barley typically occupy large portions of agricultural datasets, whereas specialty crops like ginseng or nursery fields appear far less frequently--a pattern observed in our experimental dataset as well. This imbalance frequently leads models to overfit majority classes and poorly classify minority classes [23]. Models trained with cross-entropy loss are particularly vulnerable to this issue, as the loss is inherently biased toward well-represented classes due to its weighting based on sample frequency. Consequently, accuracy for under-represented classes decreases, diminishing the overall robustness and fairness of the models. In the field of remote sensing, approaches to address class imbalance can be broadly categorized into two groups.