Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks

• URL: http://arxiv.org/abs/2112.04744v2
• Date: Fri, 10 Dec 2021 03:00:26 GMT
• Title: Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks
• Authors: Jun Wang, Zhoujing Li, Yixuan Qiao, Qiming Qin, Peng Gao, Guotong Xie
• Abstract summary: Building damage detection after natural disasters like earthquakes is crucial for initiating effective emergency response actions. Many approaches have been developed to detect damaged buildings due to earthquakes, but little attention has been paid to exploiting rich features represented in VHR images. This paper presents a novel super-pixel based approach combining Deep Neural Networks (DNN) and a modified segmentation method, to detect damaged buildings from VHR imagery.
• Score: 15.761849146985494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building damage detection after natural disasters like earthquakes is crucial for initiating effective emergency response actions. Remotely sensed very high spatial resolution (VHR) imagery can provide vital information due to their ability to map the affected buildings with high geometric precision. Many approaches have been developed to detect damaged buildings due to earthquakes. However, little attention has been paid to exploiting rich features represented in VHR images using Deep Neural Networks (DNN). This paper presents a novel super-pixel based approach combining DNN and a modified segmentation method, to detect damaged buildings from VHR imagery. Firstly, a modified Fast Scanning and Adaptive Merging method is extended to create initial over-segmentation. Secondly, the segments are merged based on the Region Adjacent Graph (RAG), considered an improved semantic similarity criterion composed of Local Binary Patterns (LBP) texture, spectral, and shape features. Thirdly, a pre-trained DNN using Stacked Denoising Auto-Encoders called SDAE-DNN is presented, to exploit the rich semantic features for building damage detection. Deep-layer feature abstraction of SDAE-DNN could boost detection accuracy through learning more intrinsic and discriminative features, which outperformed other methods using state-of-the-art alternative classifiers. We demonstrate the feasibility and effectiveness of our method using a subset of WorldView-2 imagery, in the complex urban areas of Bhaktapur, Nepal, which was affected by the Nepal Earthquake of April 25, 2015.

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