Study on Class Imbalance in Land Use Classification for Soil Erosion in Dry–Hot Valley Regions

The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to the need for soil conservation in dry–hot valley regions, this study integrated multi-source remote sensing imagery and constructed three high-precision imbalanced sample datasets on the Google Earth Engine (GEE) platform to perform land use classification. The degree of class imbalance was quantified using the imbalance ratio (IR), and the impact of sample imbalance on the classification accuracy of different land use types in a typical dry–hot valley was analyzed. The results show that (1) Feature selection significantly improved both classification accuracy and computational efficiency. The period from February to April each year, between 2018 and 2023, was identified as the optimal time window for land use classification in dry–hot valleys. (2) Constructing composite images over longer time scales enhanced classification performance: using a 2020 annual composite image combined with a Gradient Tree Boosting classifier yielded the highest accuracy, indicating that longer temporal synthesis improves classification results. (3) The effect of class imbalance on classification accuracy varied by land type: woodland (the majority class) was least affected by imbalance, whereas minority classes such as cultivated land, garden plantations, and grassland were highly sensitive to imbalance. In imbalanced scenarios, minority classes are prone to omission errors, leading to notable accuracy declines; producer’s accuracy (PA) decreased by 46%, 42%, and 25% for cultivated land, garden plantations, and grassland, respectively, as IR increased (with PA dropping faster than user’s accuracy, UA). Cultivated land was especially sensitive and frequently overlooked under high imbalance conditions compared to gardens and grasslands. Despite overall accuracy improving with higher IR, the accuracy of these minority classes dropped significantly, underscoring the importance of addressing the class imbalance in land use classification for erosion-prone areas.