Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration

Abstract

Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.