3-D Gabor Convolutional Neural Network for Damage Mapping from Post-earthquake High Resolution Images

Post-earthquake high resolution (HR) remote sensing image classification is crucial for disaster assessment and emergency rescue. 3-D convolutional neural networks (3-D CNNs) exhibit promising performance in remote sensing image classification. However, 3-D CNNs lack the theoretical underpinnings to perform multiresolution approximation for filter learning in view of the scale variance of natural objects. Gabor filtering can effectively extract multiresolution spatial information including edges and textures, which have a potential to reinforce the robustness of learned features in 3-D CNNs against the orientation and scale changes. In this paper, we propose a combined 3-D convolutional neural network and Gabor filters (GNN) method for post-earthquake HR image classification. Instead of choosing a single scale, GNN extends the spatial information to several scales by Gabor filters to take advantage of correlations among multiple scales for damage mapping. The experimental results show that GNN can reflect the multiscale information of complex scenes, obtain good classification results for mapping post-earthquake damage using HR remote sensing images.