利用建筑先验信息提升灾后建筑损毁检测精度
Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
- 用灾前光学影像训练建筑提取模型，生成高置信度伪标签
- 引入建筑引导的低不确定性伪标签优化，使损毁分割mIoU达54.28%
- 适合遥感灾害评估、多模态图像分析领域的研究人员
基于灾前光学与灾后SAR影像的精准建筑损毁评估对灾后响应与恢复规划至关重要。本文提出一种新型建筑引导伪标签学习框架，解决跨模态建筑损毁映射难题。首先，利用灾前光学影像和建筑标注训练一系列建筑提取模型，通过多模型融合与测试时增强生成伪概率，并采用低不确定性伪标签训练进一步优化分割性能。随后，在双时相跨模态影像上训练变化检测模型，结合受损建筑标签进行损伤分类。为提升分类精度，提出建筑引导的低不确定性伪标签精炼策略，利用前期建筑先验指导受损建筑的伪标签生成，降低不确定性并增强可靠性。在2025年IEEE GRSS数据融合竞赛数据集上的实验结果表明，该方法取得最高mIoU（54.28%），并在竞赛中排名第一。
原文摘要 · Abstract (English)
Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning. This study proposes a novel Building-Guided Pseudo-Label Learning Framework to address the challenges of mapping building damage from pre-disaster optical and post-disaster SAR images. First, we train a series of building extraction models using pre-disaster optical images and building labels. To enhance building segmentation, we employ multi-model fusion and test-time augmentation strategies to generate pseudo-probabilities, followed by a low-uncertainty pseudo-label training method for further refinement. Next, a change detection model is trained on bi-temporal cross-modal images and damaged building labels. To improve damage classification accuracy, we introduce a building-guided low-uncertainty pseudo-label refinement strategy, which leverages building priors from the previous step to guide pseudo-label generation for damaged buildings, reducing uncertainty and enhancing reliability. Experimental results on the 2025 IEEE GRSS Data Fusion Contest dataset demonstrate the effectiveness of our approach, which achieved the highest mIoU score (54.28%) and secured first place in the competition.
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