Research on recognition method of the damage degree in earthquake severely stricken areas based on remote sensing images

Yuanshuo Zhang; Yanbo Cao; Haoguo Du; Junzu Xu; Fanghao Zhang

doi:10.1117/12.2667542

10 February 2023 Research on recognition method of the damage degree in earthquake severely stricken areas based on remote sensing images

Yuanshuo Zhang, Yanbo Cao, Haoguo Du, Junzu Xu, Fanghao Zhang

Author Affiliations +

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522L (2023) https://doi.org/10.1117/12.2667542
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

The use of remote sensing images for earthquake disaster information extraction and disaster judgment is one of the effective ways for post-earthquake disaster recognition and assessment. In this study, the remote sensing images of Longquan Village in Longtoushan Town before and after the 2014 Ludian 6.5 magnitude earthquake in Yunnan Province were used to extract building information using the maximum likelihood estimation (MLE) in supervised classification. Combining with the vector data of building distribution plotting in Yunnan Province, we explored the rapid recognition of the damage degree in the severely-stricken areas by using the spatial change detection analysis. The research results are as follows. (1) For small areas like the earthquake severely-stricken areas, the MLE can extract the building area very simply and quickly, with a good recognition effect. (2) When applying the spatial change detection method, the severely-stricken areas can be divided into severe damage and light/moderate damage zones according to whether the area change exceeds 50%, and good recognition results can be obtained, with a correct rate above 60%.

Citation Download Citation

Yuanshuo Zhang, Yanbo Cao, Haoguo Du, Junzu Xu, and Fanghao Zhang "Research on recognition method of the damage degree in earthquake severely stricken areas based on remote sensing images", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522L (10 February 2023); https://doi.org/10.1117/12.2667542

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KEYWORDS

Earthquakes

Remote sensing

Image classification

Image fusion

Object recognition

Visualization

Statistical analysis

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