Abstract
In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient.
Highlights
Detecting damaged buildings after a massive disaster in a robust manner is a critical task, because it helps relief and rescue teams to manage related works accurately and precisely and may reduce losses
Our experimental results showed that increasing the window size and the distance parameter negatively affects the accuracy of the building damage detection methods
These results show that the change of parameters of membership functions (MFs) in a fuzzy system is very important in obtaining an accurate result
Summary
Detecting damaged buildings after a massive disaster in a robust manner is a critical task, because it helps relief and rescue teams to manage related works accurately and precisely and may reduce losses. The production of accurate building damage maps after disasters would help relief and rescue teams in emergency situations. Remote sensing (RS) data is one of the sources which can be used for generating building damage maps. As a source of the RS data, have been frequently used to produce damage maps [1]. We present a novel semi-automated decision making system based on the fuzzy theory and genetic algorithm (GA) in order to produce the building damage maps. Knowledge extraction is a necessary stage in order to convert a semi-structured problem into a structured one [2]
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