## 1 Introduction
The first is data-level oversampling strategies, and the second includes algorithmic approaches such as loss-function adjustment using focal loss or weighted loss. Oversampling methods aim to balance the class distribution by increasing the number of samples for minority classes. The most straightforward technique is simple replication of minority-class samples. However, this approach can lead to overfitting due to repeated exposure to identical data [24; 25]. To alleviate this, techniques such as SMOTE [26] and ADASYN [27] have been proposed, which generate synthetic samples by interpolating between minority-class instances. These techniques have also been successfully applied to remote sensing classification problems, demonstrating strong performance in various applications [24; 25; 28; 29]. However, such methods have been criticized for potentially generating noise or distorted spectral information that deviates from the true data distribution in high-dimensional remote sensing imagery [30; 31]. To overcome these issues, recent studies have explored GAN-based oversampling, which offers the advantage of generating more realistic samples [32; 33]. However, when the number of original minority-classsamples is extremely limited, there is a risk of generating repetitive or unrealistic synthetic images, as has been reported in the literature [34; 35]. In contrast, algorithmic approaches adjust the loss function to compensate for class imbalance by assigning greater weights to errors in minority-class predictions during training. For example, focal loss [36] and weighted cross-entropy modify the loss values to emphasize under-represented classes. These methods have the advantage of enhancing minority-class learning without altering the original data itself [37; 38]. However, they are generally highly sensitive to hyperparameter settings, and if not carefully tuned, they may lead to excessive overfitting to the minority class or instability in the training process [23; 39]. In this study, we adopt a data-level oversampling strategy, and accordingly, avoiding redundant learning and ensuring diversity in the training data become key objectives. While addressing class imbalance is important, it is equally critical to consider the geometric variability of objects in remote sensing imagery, including farmland data. Even at a fixed spatial resolution, objects in such imagery can vary significantly in scale, shape, and aspect ratio, making it challenging to process them in a consistent manner [40; 41; 42; 43]. This variability poses a fundamental obstacle to the accuracy of CNN-based analysis tasks in remote sensing, such as classification and detection. To address this issue, previous studies have proposed various approaches, including multi-scale feature pyramid frameworks [40; 41] and patch-based prediction methods that divide images into fixed-size regions [42; 43]. Among these, our study explicitly adopts the patch-based prediction strategy known as tiling, which effectively handles geometric variability while satisfying the fixed input-size requirements of CNN models. One straightforward method is resizing each parcel image to a predetermined input size. However, because farmland parcels vary significantly in shape and size, applying a uniform output size introduces inconsistent scaling factors across samples. Figure 1 illustrates this issue by comparing two example parcels and how they are transformed when applying different input preparation strategies. As shown in Figure 1d, resizing causes images with different native resolutions to be rescaled disproportionately, distorting their original textures and spatial proportions. This inconsistency undermines the uniformity of image preprocessing and hampers the model's ability to learn meaningful visual features. As a result, the visual distinction among classes is reduced, especially for crop types with fine-grained structural differences, making classification more difficult.