Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

Abstract

Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%.

A Literature Review on the Development of Remote Sensing in Damage Detection of Civil Structures

“…However, this type of data is not always accessible. A report by [49] demonstrates the ability of radar data for detecting damaged areas under cloud and shadow conditions, as shown in Fig. 19.…”

Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing

“…Table 2 reports the S-1 specifications. Data from the S-1 C-band (5.405 GHz) images satellite has the tremendous ability to support a variety of applications such as landslide detection, mapping and description, landslide monitoring, mining activity, subsidence, and earthquake phenomena (Hasanlou et al, 2021).…”

“…Continuously by processing SAR satellite remote sensing imagery, valuable information about the Earth can be examined at different scales with the least possible time and cost in a long period of time. SAR satellites remote sensing has made it possible to make extensive use of high-resolution, high-spatial, multi-axis data sets (Hasanlou et al, 2021). Specifically, several methods have been designed and developed based on a SAR (Sentinel-1, etc.)…”

Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

“…The study area is located in the west of Iran in the Kermanshah province near the Iraqi border. Sarpol-e Zahab and the surrounding urban and rural areas were severely damaged by the M7.3 earthquake, which was the strongest seismic event since 1967 near the Iran-Iraq border (Figure 1) [47][48][49]. From the geological point of view, the M7.3 Sarpol-e Zahab earthquake occurred in the northwest Zagros fold-thrust area (Figure 2).…”