Abstract
Earthquakes are one of the most destructive natural disasters. Efficiently and quickly acquiring building earthquake damage information can help to reduce the casualties after an earthquake. In this paper, for convenience, speed, and precision, building damage information is extracted using a single post-earthquake PolSAR image. In PolSAR images, the undamaged parallel buildings characterized by double-bounce scattering are different from the collapsed buildings characterized by volume scattering, but the undamaged oriented buildings are very similar to collapsed buildings because of their scattering mechanism ambiguity in the early traditional model-based decomposition. Therefore, the collapsed buildings are difficult to extract accurately. In this paper, the scheme of polarization orientation angle (POA) compensation is employed to enhance the double-bounce scattering power of the oriented buildings, and the difference in the relative contribution change rate of scattering components before and after POA compensation is proposed to further enhance the difference between collapsed buildings and oriented buildings, in order that the collapsed buildings can be extracted more accurately. The “4.14” Yushu earthquake, which occurred in Yushu County, Qinghai province of China, is used as the case study to test the proposed method, and an airborne high-resolution PolSAR image of the urban region of Yushu County is used in the experiment. The experimental results show that the accuracy of building damage information extraction can be improved by the use of the proposed method, compared with the traditional polarimetric classification.
Highlights
Earthquakes can give rise to great damage to life and property
Experimental results According to the process of building earthquake damage information extraction shown in Fig. 3, the Wishart supervised classification method was performed on the polarimetric SAR (PolSAR) data after polarization orientation angle (POA) compensation
The two kinds of buildings were reclassified based on Eq (13), as described in “Analysis of building scattering components in earthquake-stricken areas” section, and clustering based on the Wishart distance was performed on the initial classification results, and the final classification results of the oriented buildings and the collapsed buildings were obtained
Summary
Earthquakes can give rise to great damage to life and property. In an earthquake, most of the casualties are caused by collapsed buildings, so building damage information extraction is the main task of earthquake damage information investigation (Trianni and Gamba 2009). After the 2011 Tohoku earthquake, many scholars analyzed and studied the earthquake damage characteristics and the disaster investigation using airborne and satellite PolSAR data. Most of these studies were carried out mainly based on the changes of polarimetric features between pre- and post-earthquake using multi-temporal PolSAR data (Chen and Sato 2013; Park et al 2013; Sato et al 2012; Watanabe et al 2012). Guo et al (2012) and Li et al (2012) introduced the circular polarization correlation coefficient ρ and proposed the H–α–ρ method to extract collapsed building information using RADARSAT-2 polarimetric data after the “4.14” Yushu earthquake. Earthquake damage assessment using only a single post-earthquake PolSAR image has attracted the attention of more and more researchers. Guo et al (2012) and Li et al (2012) introduced the circular polarization correlation coefficient ρ and proposed the H–α–ρ method to extract collapsed building information using RADARSAT-2 polarimetric data after the “4.14” Yushu earthquake. Zhao et al (2013) introduced the texture parameter of homogeneity to improve the H– α–ρ method using high-resolution airborne PolSAR data. Shen et al (2015) used the method of image retrieval based on feature template matching to extract the collapsed building information. Zhai et al (2016b) used the two parameters of the normalized difference of the dihedral component (NDDC) and the HH–HV correlation coefficient (ρHHHV) to correct the result of Wishart supervised classification and extracted the collapsed buildings, and the high accuracy for building earthquake damage information extraction was acquired eventually
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