Building Earthquake Damage Recognition Based on Frequency Domain Texture Features from PolSAR Data

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. The spatial frequency of SAR images can be clearly rendered in the frequency domain. In this study, we propose a new texture feature based on Fourier transform, namely the sector texture feature of the Fourier amplitude spectrum (STFFAS), to solve the overestimate of damage of buildings, which are caused by earthquakes. STFFAS can well describe the difference in texture between oriented buildings and collapsed buildings and accurately recognize the two types of buildings. The STFFAS index can be defined as follows:

             (1)

where ‘FFT’, ‘std’, ‘mean’ and ‘lg’ represent the function of 2D fast Fourier transform, standard deviation, mean values and logarithm to the base 10, respectively; ‘real’ and ‘imag’ represent the real parts and imaginary parts of complex numbers, respectively. Meanwhile, based on the Yamaguchi four-component decomposition method and the STFFAS texture feature 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 overall accuracy of correct building damage recognition with STFFAS is 81.30%. The Producer‘s Accuracy (PA) of damaged buildings, which is the correct recognition rate of collapsed buildings, is 81.06%; and the PA of undamaged buildings, which is the correct recognition rate of undamaged buildings, reaches 81.42%. Compared with the traditional polarimetric decomposition method, 70.18% 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. This result well confirmed that the texture feature in frequency domain is effective for building damage recognition.