Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)
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DOI：
10.3390/rs13214272
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Davoud Omarzadeh;S. Karimzadeh;M. Matsuoka;B. Feizizadeh
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文献类型：
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作者：
Davoud Omarzadeh;S. Karimzadeh;M. Matsuoka;B. Feizizadeh
本研究旨在使用极高分辨率（VHR）事件后WorldView-2图像和基于对象的图像分析（OBIA）方法对伊朗西部破坏性M7.3克尔曼沙阿地震（2017年11月12日）后的城市地区及其周围物体进行分类。多光谱（MS）波段（~2米）的空间分辨率首先使用全色锐化技术，提供了一个解决方案，通过融合全色（PAN）和MS波段的信息，以产生全色锐化图像的空间分辨率约为50厘米。在应用分割过程之后，分类步骤被认为是提取目标特征的主要过程。上述分类方法包括应用光谱和形状指数。然后，分类定义如下：类型1（定居区）是塌陷区，非塌陷区，营地;类型2（植被区）是果园，耕地，城市绿色空间;和类型3（杂项区）是岩石，河流和裸露的土地。由于OBIA导致图像对象的空间特征的集成，我们还旨在评估基于对象的特征在半自动方法中用于损伤评估的效率。为了这个目标，图像上下文评估算法（例如，纹理参数、形状和紧密度）连同光谱信息（例如，亮度和标准偏差）被应用在集成方法中。与训研所（联合国训练研究所）提供的倒塌建筑物参考地图相比，分类结果令人满意。此外，在应用OBIA后统计了临时营地的数量，表明截至2018年11月17日，为无家可归者建立了10，249个帐篷或临时避难所。根据受损人口总数，可以估计应急设备、罐头食品和水瓶等基本资源。该研究通过应用不同的基于对象的图像分析技术并在半自动方法中评估其效率，从而为这些方法在其他全球案例研究中的有效应用提供支持，从而为遥感科学的发展做出了重大贡献。
This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.