Accurate building segmentation from high-resolution RGB imagery remains challenging due to spectral similarity with non-building features, shadows, and irregular building geometries. In this study, we present a comprehensive deep learning framework for multiscale building segmentation using RGB aerial and satellite imagery with spatial resolutions ranging from 0.4m to 2.7m. We curate a diverse, multi-sensor dataset and introduce feature-augmented inputs by deriving secondary representations including Principal Component Analysis (PCA), Visible Difference Vegetation Index (VDVI), Morphological Building Index (MBI), and Sobel edge filters from RGB channels. These features guide a Res-U-Net architecture in learning complex spatial patterns more effectively. We also propose training policies incorporating layer freezing, cyclical learning rates, and SuperConvergence to reduce training time and resource usage. Evaluated on a held-out WorldView-3 image, our model achieves an overall accuracy of 96.5%, an F1-score of 0.86, and an Intersection over Union (IoU) of 0.80, outperforming existing RGB-based benchmarks. This study demonstrates the effectiveness of combining multi-resolution imagery, feature augmentation, and optimized training strategies for robust building segmentation in remote sensing applications.

本研究针对高分辨率RGB图像中建筑物分割的困难，如光谱相似性、阴影和不规则建筑几何形状，提出了一种全面的深度学习框架。通过引入特征增强输入和优化训练策略，我们的模型在WorldView-3图像上的表现显著优于现有RGB基准，达到96.5%的总体准确率和0.80的交并比，证明了其在遥感应用中强大的建筑分割能力。

用于高分辨率无人机和卫星图像多尺度建筑分割的特征增强深度网络