A Deep Learning-Based Solution to the Class Imbalance Problem in High-Resolution Land Cover Classification

Class imbalance (CI) poses a significant challenge in machine learning, characterized by a substantial disparity in sample sizes between majority and minority classes, leading to a pronounced “long-tail effect” in statistical distributions and subsequent inference processes. This issue is particularly acute in high-resolution land cover classification within arid regions, where CI tends to bias classification outcomes towards majority classes, often at the expense of minority classes. Recent advancements in deep learning have opened new avenues for tackling the CI problem in this context, focusing on three key aspects: the semantic segmentation model, loss function design, and dataset composition. To address this issue, we propose the high-resolution U-shaped mamba network (HRUMamba), which integrates multiple innovations to enhance segmentation performance under imbalanced conditions. Specifically, HRUMamba adopts a pre-trained HRNet as the encoder for capturing fine-grained local features and incorporates a modified scaled visual state space (SVSS) block in the decoder to model long-range dependencies effectively. An adaptive awareness fusion (AAF) module is embedded within the skip connections to enhance target saliency. Additionally, we introduce a synthetic loss function that combines cross-entropy loss, Dice loss, and auxiliary loss to improve optimization stability. To quantitatively assess multi-class imbalance, we introduce the coefficient of variation (CV) as a novel evaluation metric. Experimental results on the ISPRS Vaihingen and Minqin datasets demonstrate the robustness and effectiveness of HRUMamba in mitigating CI. The proposed model achieves the highest mF1 scores of 92.25% and 89.88%, along with the lowest CV values of 0.0445 and 0.0574, respectively, outperforming state-of-the-art methods. These innovations underscore the potential of HRUMamba in advancing high-resolution land cover classification in imbalanced datasets.