用多光谱卫星图做洪水分割，提出渐进式交叉注意力网络提升精度。
Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery
- 分步引入自注意力与交叉注意力，融合多光谱特征。
- 在两个数据集上达到最高0.815的交并比（IoU）。
- 适合遥感洪水监测、灾害应急响应等场景使用。
近年来，深度学习与遥感技术的结合彻底改变了洪水等自然灾害的监测与管理方式。然而，现有基于遥感数据的洪水分割方法常忽视多光谱信息间的相关性。本文提出一种渐进式交叉注意力网络（ProCANet），通过逐步应用自注意力与交叉注意力机制，对多光谱特征进行优化组合，以提升洪水分割效果。模型在Sen1Floods11数据集及为印度尼西亚Citarum河流域构建的定制洪水数据集上与当前先进方法对比，取得最高0.815的交并比（IoU）得分。通过消融实验对比不同模态下是否启用注意力机制，验证了该方法的有效性，为提升遥感洪水分析精度提供了新路径。
原文摘要 · Abstract (English)
In recent years, the integration of deep learning techniques with remote sensing technology has revolutionized the way natural hazards, such as floods, are monitored and managed. However, existing methods for flood segmentation using remote sensing data often overlook the utility of correlative features among multispectral satellite information. In this study, we introduce a progressive cross attention network (ProCANet), a deep learning model that progressively applies both self- and cross-attention mechanisms to multispectral features, generating optimal feature combinations for flood segmentation. The proposed model was compared with state-of-the-art approaches using Sen1Floods11 dataset and our bespoke flood data generated for the Citarum River basin, Indonesia. Our model demonstrated superior performance with the highest Intersection over Union (IoU) score of 0.815. Our results in this study, coupled with the ablation assessment comparing scenarios with and without attention across various modalities, opens a promising path for enhancing the accuracy of flood analysis using remote sensing technology.
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