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%.
Highlights
Earth is constantly undergoing dynamic processes such as natural disasters as well as anthropogenic changes to Earth’s surface [1,2,3]
This research proposes a novel damage detection method based on both optical and SAR imagery in an unsupervised framework with the following properties: (1) extraction of urban areas in an unsupervised framework, (2) detecting damaged areas and classifying them according to different levels based on the coherence spectral signature, (3) an unsupervised damage detection algorithm with no need for the initial parameter setting, (4) easy implementation with low computational complexity, (5) using free-access optical (Sentinel-2) and SAR (Sentinel-1) imagery with medium spatial resolution and good temporal resolution
We incorporated four multitemporal datasets acquired on 30 October, 11 November, 17 November, and 5 December 2017
Summary
Earth is constantly undergoing dynamic processes such as natural disasters as well as anthropogenic changes to Earth’s surface [1,2,3]. During recent development of remote sensing satellites with high spectral, spatial, and temporal resolutions [16,17] Such techniques play a key has allowed researchers to widely employ multitemporal image datasets with high spectral, role in environmental many applications, especially damage spatial, and temporalmonitoring resolutionsin[16,17]. The use of radar imagery for damage detection has been considered by researchers and much research has been presented [10,14,24,28,29,30,31,32,33,34,35,36] They were focused mainly on the indexing of two temporal datasets, ignoring many factors such as the effects of noise and vegetation on the coherency products. This research proposes a novel damage detection method based on both optical and SAR imagery in an unsupervised framework with the following properties: (1) extraction of urban areas in an unsupervised framework, (2) detecting damaged areas and classifying them according to different levels based on the coherence spectral signature, (3) an unsupervised damage detection algorithm with no need for the initial parameter setting, (4) easy implementation with low computational complexity, (5) using free-access optical (Sentinel-2) and SAR (Sentinel-1) imagery with medium spatial resolution and good temporal resolution (with the latter having small sensitivity with respect to clouds and light rain and the capability of being operated by day and by night)
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