Damaged buildings classification using residual network

Liuding He; Shuyi Pi; Haoze Wu

doi:10.1117/12.2640899

13 October 2022 Damaged buildings classification using residual network

Liuding He, Shuyi Pi, Haoze Wu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228714 (2022) https://doi.org/10.1117/12.2640899
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Classification of damaged buildings post hurricanes could be a cinch with the implementation of deep learning networks. Many researchers have engaged in conducting such tasks with the help of neural networks on satellite images of posthurricane buildings, while few of them grant appropriate attention to the data preprocessing methods even considering some unique properties of the satellite images. In this paper, we investigate several traditional data preprocessing approaches and some novel image preprocessing methods while making comparisons and analyses. Eventually, we could reach an accuracy over 98% with appropriate preprocessing approaches with a simplified ResNet network. Furthermore, we also analyze the effectiveness of the data augmentation method for damaged building classification, including geometric transformation, photometric transformation, and image mixing.

Citation Download Citation

Liuding He, Shuyi Pi, and Haoze Wu "Damaged buildings classification using residual network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228714 (13 October 2022); https://doi.org/10.1117/12.2640899

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KEYWORDS

RGB color model

Satellites

Satellite imaging

Earth observing sensors

Buildings

Data modeling

Image fusion

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