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Abstract
Abstract. In this study, we have analysed the optical and SAR images both to detect the collapsed building automatically with the use of the cloud-based programming environment Google Colab Cloud environment. We have used the existing digital map of buildings which were provided by Here Maps Company, for each building feature, the histograms were generated both for optical and SAR images, the unmatched histograms on the optical image were mainly the destroyed buildings and newly established tent areas for the people who lost their homes. In the method, the most recent (before and after) optical images of the earthquake zone are taken. Some pre-processing steps were performed including principal component analysis, K-Means clustering. Then, the statistical values of area overlap with the building vectors are calculated and the threshold values are determined. SAR images are used to refine the results. he used optical satellite images are Worldview images with 30 cm GSD, and for SAR images, Sentinel 1 C band and ICEYE X band SAR images are used. Sentinel 1 and ICEYE images are provided from ESA.
Highlights
Earthquakes, one of the biggest natural disasters; It affects the regions where it occurs to a great extent and poses a great threat to the safety of human life
As the optical and SAR images have different types of characteristics, advantages and disadvantages compare to the other, we have developed an approach that uses different parameters for each, and the parameters are calculated automatically without any user interaction
Due to difficulty to analyse the histograms of the raw images, the images were first converted to principal components, clustered with K-Means method
Summary
Earthquakes, one of the biggest natural disasters; It affects the regions where it occurs to a great extent and poses a great threat to the safety of human life. Remote sensing science is an essential to do damage analysis with both optical and SAR satellite imagery (Brunner et al, 2010). The information by the phase and amplitude of the SAR signal is used for various purposes such as crustal deformation analysis (Stramondo et al, 2016), classification studies (Chini et al, 2009; Pulvirenti et al, 2016), or damage mapping in post-emergency scenarios (Piscini et al, 2017; Stramondo et al, 2006). According to different input data, there are two types of methods of obtaining damage status from remotely sensed images: methods based on pre- and post-earthquake historical images and methods based on single-date images after the earthquake. Besides large faults, small to medium sized earthquakes on secondary and main faults are as important as large earthquakes in terms of information content (Massonnet et al, 1993)
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