Two New Polarimetric Feature Parameters for the Recognition of the Different Kinds of Buildings in Earthquake-Stricken Areas Based on Entropy and Eigenvalues of PolSAR Decomposition

Abstract

Rapidly and accurately obtaining collapsed building information for earthquake-stricken areas can help to effectively guide the implementation of the emergency response and can reduce disaster losses and casualties. This work is focused on rapid building earthquake damage detection in urban areas using a single post-earthquake polarimetric synthetic aperture radar (PolSAR) image. In an earthquake-stricken area, the buildings include both damaged buildings and undamaged buildings. The undamaged buildings are made up of both parallel buildings, whose array direction is parallel to the flight direction, and oriented buildings, whose array direction is not parallel to the flight direction. The damaged buildings after an earthquake are made up of completely collapsed buildings and residual damaged parallel walls and oriented walls of the damaged buildings. Therefore, we divide the buildings in earthquake-stricken areas into five kinds: intact parallel buildings, damaged parallel walls, collapsed buildings, intact oriented buildings, and damaged oriented walls. The two new polarimetric feature parameters of λ_H and H_λ are proposed to recognize the five kinds of buildings, and the Wishart supervised classification method is employed to further improve the extraction accuracy of the damaged buildings and undamaged buildings.

Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing Technique

“…Nevertheless, the rigorous derivation in Eq. (21) validates that G4U cannot always enhance the double-bounce scattering nor strengthen the surface scattering power unless we adaptively integrate G4U and its duality, i.e., DG4U, for EG4U based on another boundary condition BC 1 . As expressed in Eq.…”

“…Therefore, by analyzing the power of double-bounce scattering and surface scattering before and after the event, we can achieve an efficient monitoring of the disasters. This simple strategy has been successfully adopted in the polarimetric microwave remote sensing of tsunami/earthquake [12][13][14][15][16][17][18][19][20][21].…”

A Novel Texture Feature Based on Fourier Transform for Building Damage Recognition from PolSAR Data

“…(10) Zhai et al developed two new polarimetric features λ_H and H_λ on the basis of entropy and eigenvalues of a T3 matrix to discriminate five different types of buildings in disaster areas. (11) Liu et al established a model based on components obtained by Touzi decomposition for damage assessment mapping in builtup areas. (12) Zhai et al developed a new polarimetric feature, the variable coefficient of angle domains based on the Fourier amplitude spectrum parameter (CV_AFI) to assess the degree of building earthquake damage.…”

Extraction of Earthquake Damage Information and Mapping of Buildings from Single Post-earthquake Polarimetric Synthetic Aperture Radar Image Based on Polarimetric Decomposition and Texture Features

“…(15,16) Since polarimetric features are the main information source of PolSAR data, the extraction of information of damaged buildings from single-temporal post-earthquake PolSAR data is mainly based on the polarimetric target decomposition model and polarimetric features parameters. (17)(18)(19) However, because texture features of PolSAR images are as important as polarimetric features in PolSAR data, they have an even higher identification efficiency than polarimetric features in many cases. Therefore, many scholars identify the damage of buildings from the texture features when using the polarization information in PolSAR data; this can yield better identification results of building damage caused by earthquakes.…”