Building Damage Detection Using Object-Based Image Analysis and ANFIS From High-Resolution Image (Case Study: BAM Earthquake, Iran)

Abstract

Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel- and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit ( $\text {rect\_fit}$ ), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.