| Title | Multi-Step Feature Fusion for Natural Disaster Damage Assessment on Satellite Images |
| Publication Type | Journal Article |
| Year of Publication | 2024 |
| Authors | Żarski M , Miszczak J |
| Journal | IEEE Access |
| Start Page | 140072 - 140081 |
| Date Published | 09/2024 |
| Keywords | computer vision , damage state assessment , Machine learning , remote sensing |
| Abstract | 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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| DOI | 10.1109/ACCESS.2024.3459424 |
