Building Earthquake Damage Mapping from Post-event PolSAR Data Based on Polarimetric Decomposition and Texture Features

<p>Buildings are the main places for people to live and work as well as the most important economic entities in urban areas. The collapse of buildings caused by destructive earthquakes often caused severe casualties and economic losses. After an earthquake, the assessment of building damage is one of the most important tasks in earthquake emergency response. Accurate assessment of building damage will be essential in making plans of emergency responses. Four-Polarimetric Synthetic Aperture Radar (PolSAR) data has the advantages of Synthetic Aperture Radar (SAR) imaging that is not occluded by sunlight and clouds, it also contains the most abundant information of four polarimetric channels. Due to the large amount of information in PolSAR data, only a single post-earthquake PolSAR image can be used to identify building damage of post-earthquake. It is easy to overestimate the number of collapsed buildings and the damage degree of earthquakes only using a traditional polarimetric decomposition method for PolSAR data. The layout of urban buildings can be diverse. Buildings can stand in parallel in typical SAR imaging with strong scattering features, there are also some oriented standing buildings with lower scattering intensity and with similar scattering characteristics of collapsed buildings, thus these oriented buildings are often misconstrued as collapsed buildings. In this study, we propose a new texture feature, namely mean standard deviation (MSD) index of texture feature based on Gray-level Co-occurrence Matrix (GLCM), to solve the overestimate of damage of buildings, which are caused by earthquakes. The MSD index can be defined as follows:</p> <p>&#160; &#160; &#160; &#160; &#160; &#160;<img src="" alt="" width="316" height="46" /> &#160; (1)</p> <p>where I_SAR is the intensity image of PolSAR data, and mean (&#8226;) and variance (&#8226;) represent the calculation of mean values and variance values based on GLCM for (&#8226;), respectively. Meanwhile, based on the improved Yamaguchi four-component decomposition method and the MSD index parameter, we develop a solution to identify the damage of buildings only using a single post-earthquake PolSAR image. The Ms7.1 Yushu earthquake, which occurred in Yushu County of China on 14th April, 2010, is used as a study case to carry out the experiment with 75000 undamaged and damaged building samples. With the proposed method, the experimental results show 82.43% identification accuracy for damaged buildings and 80.30% identification accuracy for undamaged buildings. Compared with the traditional polarimetric decomposition method, 66.89% standing buildings are successfully isolated from the mixture of collapsed buildings. Therefore this new method has greatly improved the accuracy and reliability of extracting damage information of buildings.</p>