Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery. While most deep learning methods rely on spatial-domain features, frequency-domain representations can capture complementary structural cues such as debris patterns and collapse-induced textures. This study presents a controlled comparison of spatial-domain, frequency-domain, and dual-domain deep learning approaches for multi-class building damage classification using post-disaster imagery from the xView2 (xBD) dataset. To ensure fairness, all models are built on an EfficientNet-B0 backbone and trained under identical settings, differing only in their input representations and fusion strategies. Performance is evaluated using accuracy, macro F1-score, per-class metrics, and confusion matrices. Results show that dual-domain models provide measurable improvements over single-domain approaches. The dual spatial configuration achieves the highest test accuracy (0.4688) and lowest loss, while the spatial-only model attains the best macro F1-score (0.4254), indicating more balanced class performance. In contrast, frequency-only models perform worst and exhibit overfitting, suggesting limited generalization. Despite these gains, all models struggle to detect subtle damage levels, particularly the Minor class, due to class imbalance and fine-grained visual ambiguity. While dual-domain approaches improve detection of severe damage, challenges remain. These findings highlight the benefits and limitations of hybrid representations and motivate future work on data balancing, advanced fusion, and regularization.

翻译：从卫星图像中对建筑损伤进行快速评估对于有效的灾害响应与恢复至关重要。尽管多数深度学习方法依赖空间域特征，但频率域表示能够捕捉互补的结构线索，如废墟模式和倒塌引起的纹理。本研究针对xView2（xBD）数据集中的灾后图像，对基于空间域、频率域及双域深度学习方法的多类建筑损伤分类进行了受控比较。为确保公平性，所有模型均以EfficientNet-B0为骨干网络，并在相同设置下训练，仅输入表示和融合策略不同。性能评估采用准确率、宏平均F1分数、各类别指标及混淆矩阵。结果表明，双域模型相比单域方法有可测量的改进。双空间配置取得了最高的测试准确率（0.4688）和最低损失，而纯空间模型获得了最佳宏平均F1分数（0.4254），表明其类别性能更为均衡。相比之下，纯频率模型表现最差且存在过拟合，表明泛化能力有限。尽管取得这些进展，所有模型在检测细微损伤级别（尤其是Minor类别）时仍存在困难，原因是类别不平衡和细粒度视觉模糊性。虽然双域方法改善了对严重损伤的检测，但挑战依然存在。这些发现凸显了混合表示的优势与局限性，并为未来在数据平衡、先进融合及正则化方面的研究提供了动力。