通过多阶段特征融合提升灾后建筑损毁评估精度
Multi-step feature fusion for natural disaster damage assessment on satellite images
- 在多层网络中横向纵向融合灾前灾后卫星图特征
- 在Vision Transformer上实现超3个百分点的准确率提升
- 适配任意CNN模型，适用于大规模灾损评估场景
自然灾害后快速准确评估建筑物损毁状态对制定精准救援和后续恢复措施至关重要，直接影响受灾人员安全与灾后恢复成本。利用计算机视觉中的机器学习方法可显著提升评估质量。本文提出一种基于新型多阶段特征融合网络的损毁评估方法，通过在灾前与灾后大尺度卫星图像间，于卷积神经网络（CNN）的多个层级进行水平与垂直方向的特征融合，实现建筑损毁状态分类。设计了可嵌入任意CNN模型的通用融合模块（Fuse Module），用于处理图像对分类任务。在公开的大规模数据集IDA-BD和xView2上验证表明，该方法可有效提升现有最先进架构性能，在Vision Transformer模型上实现超过3个百分点的准确率提升。
原文摘要 · Abstract (English)
Quick and accurate assessment of the damage state of buildings after natural disasters is crucial for undertaking properly targeted rescue and subsequent recovery operations, which can have a major impact on the safety of victims and the cost of disaster recovery. The quality of such a process can be significantly improved by harnessing the potential of machine learning methods in computer vision. This paper presents a novel damage assessment method using an original multi-step feature fusion network for the classification of the damage state of buildings based on pre- and post-disaster large-scale satellite images. We introduce a novel convolutional neural network (CNN) module that performs feature fusion at multiple network levels between pre- and post-disaster images in the horizontal and vertical directions of CNN network. An additional network element - Fuse Module - was proposed to adapt any CNN model to analyze image pairs in the issue of pair classification. We use, open, large-scale datasets (IDA-BD and xView2) to verify, that the proposed method is suitable to improve on existing state-of-the-art architectures. We report over a 3 percentage point increase in the accuracy of the Vision Transformer model.
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