针对洪水灾后建筑损毁评估，提出新模型与半监督方法提升小变化识别能力。
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images
- 引入先验注意力机制的SPAUNet模型，增强对细微变化的捕捉能力。
- 在xBD数据集上，受损分类召回率达79.10%，F1值达71.32%，优于传统变化检测方法。
- 利用未标注数据类别分布构建一致性正则化，显著提升半监督学习效果。
灾后建筑损毁评估对快速响应和重建规划至关重要。现有研究忽视了灾害评估（DA）与变化检测（CD）在神经网络设计上的差异。本文指出两大关键区别：1）DA卫星图像中建筑变化特征更微弱；2）DA数据集面临更严重的数据稀缺与标签不平衡。为此，模型架构方面，研究了注意力机制在灾后评估任务中的基准性能，并提出简单先验注意力UNet（SPAUNet），以增强对细微变化的识别能力；半监督学习策略方面，构建了四种基于图像级标签类别参考分布的一致性训练组合。在xBD数据集的洪灾事件实验中，SPAUNet在监督学习下表现优异，受损分类召回率达79.10%，F1得分为71.32%，优于传统变化检测方法。半监督实验表明，图像级一致性正则化具有积极作用，使用伪标签构建参考分布效果最佳，证明了利用大量未标注数据的类别分布进行半监督学习的潜力。本研究厘清了DA与CD任务的本质差异，初步探索了基于先验注意力机制与图像级一致性正则化的模型设计策略，建立了新的灾后评估任务基准方法。
原文摘要 · Abstract (English)
Post-flood building damage assessment is critical for rapid response and post-disaster reconstruction planning. Current research fails to consider the distinct requirements of disaster assessment (DA) from change detection (CD) in neural network design. This paper focuses on two key differences: 1) building change features in DA satellite images are more subtle than in CD; 2) DA datasets face more severe data scarcity and label imbalance. To address these issues, in terms of model architecture, the research explores the benchmark performance of attention mechanisms in post-flood DA tasks and introduces Simple Prior Attention UNet (SPAUNet) to enhance the model's ability to recognize subtle changes, in terms of semi-supervised learning (SSL) strategies, the paper constructs four different combinations of image-level label category reference distributions for consistent training. Experimental results on flood events of xBD dataset show that SPAUNet performs exceptionally well in supervised learning experiments, achieving a recall of 79.10% and an F1 score of 71.32% for damaged classification, outperforming CD methods. The results indicate the necessity of DA task-oriented model design. SSL experiments demonstrate the positive impact of image-level consistency regularization on the model. Using pseudo-labels to form the reference distribution for consistency training yields the best results, proving the potential of using the category distribution of a large amount of unlabeled data for SSL. This paper clarifies the differences between DA and CD tasks. It preliminarily explores model design strategies utilizing prior attention mechanisms and image-level consistency regularization, establishing new post-flood DA task benchmark methods.
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