Improving remote sensing scene classification with data augmentation techniques to mitigate class imbalance

High-resolution remote sensing imagery is a powerful tool that provides massive information about ground objects. However, conventional methods often fail to achieve satisfactory results for complex urban scene classification. This is attributed to the fact that conventional methods are unable to meet the requirements of high-accuracy remote sensing image scene classification (RSSC) and are hindered by challenges such as limited labeled samples and class imbalance, which may lead to classification bias in classifiers. On the contrary, deep learning-based RSSC represents an important approach for understanding semantic information. This paper explores the feasibility of mitigating classification bias by reducing the imbalance ratio (IR) of the dataset. First, a class-imbalanced dataset was constructed using very high-resolution (VHR) images, labeled into nine land use/land cover (LULC) categories. Second, comprehensive data augmentation techniques (mirroring, rotation, cropping, Hue, Saturation, Value (HSV) perturbation, and gamma transformation) were applied, successfully reducing the dataset's IR from 9.38 to 1.28. Subsequently, four architectures, MobileNet-v2, ResNet101, ResNeXt101_32 × 32d, and Transformer, were trained and evaluated on both class-balanced and class-imbalanced datasets. The results indicate that the classification bias caused by class imbalance was alleviated, significantly improving the classifier's performance. Specifically for the most severely underrepresented category (intersections), precision and recall improvements reached up to 128% and 102%, respectively, narrowing the gap with other categories and reducing classification bias. Furthermore, the average Kappa and overall accuracy (OA) increased by 11.84% and 12.97%, respectively, with reduced standard deviations in recall and precision, demonstrating enhanced model stability.