Investigation and Extraction of Building Demolitions due to Earthquake using High Resolution Satellite Images

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Hoseinzadeh Dehabadi Ali Asghar; ARGANI MEYSAM; Daevishi Bolorani Ali

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Journal Paper

Paper Information

Journal: ENVIRONMENTAL HAZARDS MANAGEMENT Year:2019 | Volume:6 | Issue:3 Page(s): 239-257

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Information Journal Paper

Title

Investigation and Extraction of Building Demolitions due to Earthquake using High Resolution Satellite Images

Author(s)

Hoseinzadeh Dehabadi Ali Asghar | ARGANI MEYSAM | Daevishi Bolorani Ali | Issue Writer Certificate

Keywords

EarthquakeQ1

Building Demolition MapQ1

Multilayer Perceptron (MLP) Neural NetworkQ1

Adaptive Neuro-Fuzzy Inference System (ANFIS)Q1

and Support Vector Machines (SVM)Q1

Abstract

Earthquake is one of the natural disasters that if occurs strongly in high population areas, will create great human catastrophe. Earthquake can provide considerable life and financial devastating effects, especially in urban regions. Observation of damaged buildings map is crucial for crisis management experts and helps them guide rescue teams to damaged locations in short period of time. Remote sensing and geographic information system is an efficient tool of rapid survey of condition of damaged buildings after the Earthquake in urban regions. This research has been conducted with the aim of identification of demolished buildings due to Earthquake by very high resolution satellite images and comparison of available efficient methods. To achieve these goals, very high resolution satellite images of Bam city, before and after the Earthquake, and the observed damage map of the region were used. In this study, the best and the most appropriate textural indices were chosen after calculation of textural features of images by statistical analysis of logistic regression and correlation. Then, the condition of buildings demolition was classified by optimum obtained textural values and implementing Multilayer Perceptron (MLP) neural network systems, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM). Finally, the accuracy of all the presented techniques were compared with each other and the best proposed technique was selected and presented. According to the results, all the three MLP, SVM and ANFIS methods were good for classification of degrees of buildings demolition, but ANFIS method was better with 1% in overall accuracy, 4% in kappa coefficient, and 1. 7% in RMSE.

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APA: Copy

Hoseinzadeh Dehabadi, Ali Asghar, ARGANI, MEYSAM, & Daevishi Bolorani, Ali. (2019). Investigation and Extraction of Building Demolitions due to Earthquake using High Resolution Satellite Images. ENVIRONMENTAL HAZARDS MANAGEMENT, 6(3 ), 239-257. SID. https://sid.ir/paper/407924/en

Vancouver: Copy

Hoseinzadeh Dehabadi Ali Asghar, ARGANI MEYSAM, Daevishi Bolorani Ali. Investigation and Extraction of Building Demolitions due to Earthquake using High Resolution Satellite Images. ENVIRONMENTAL HAZARDS MANAGEMENT[Internet]. 2019;6(3 ):239-257. Available from: https://sid.ir/paper/407924/en

IEEE: Copy

Ali Asghar Hoseinzadeh Dehabadi, MEYSAM ARGANI, and Ali Daevishi Bolorani, “Investigation and Extraction of Building Demolitions due to Earthquake using High Resolution Satellite Images,” ENVIRONMENTAL HAZARDS MANAGEMENT, vol. 6, no. 3 , pp. 239–257, 2019, [Online]. Available: https://sid.ir/paper/407924/en

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