Duan F.,Capital Normal University |
Gong H.,Key Laboratory of 3D Information Acquisition and Application of MOE |
Zhao W.,Capital Normal University
2010 18th International Conference on Geoinformatics, Geoinformatics 2010 | Year: 2010
Collapsed houses in city are one of the most important destructions after earthquake. The area, amount and rate of collapsed houses are the most essential data sources to decide how to reconstruct in the disaster area after earthquake. It is often difficult to access high-resolution remote sensing satellite images in time only with satellite sensors in the area. For example, after the Wenchuan earthquake on May 12 2008, high-resolution satellite remote sensing images had not been acquired after a few days in which all actions have to be taken for disaster mitigation and relief. With various aviation flight platforms, the aerial remote sensing images are obtained in time at a relatively short period of time with better weather in the earthquake zones. So how to rapidly identify and detect the damage building is the most important problem. In this paper, through the texture changes of aerial remote sensing images, the method of housing collapsed automatically determination is expounded. The approach for detecting the damaged buildings after earthquake is different to visual identification of the post-event aerial images. The approach utilizes the texture difference between the buildings and the collapsed buildings. It is based on an idea that if a building collapsed, it will produce more complex texture. After we calculate the texture complexity parameter, the building may has collapsed if the texture complexity parameter is beyond threshold. After Wenchuan earthquake, the aerial remote sensing images are testified by this way in Beichuan, Wenchuan and Anxian. The rate of houses collapsed and the ratio of the edge of texture are strongly correlated. If the rate is beyond 0.15, the house will collapse over eighty percent, and if the rate is under 0.10, the percentage drops. Therefore, through the ratio of the edge of pixels and the number of regional architecture of the region pixels, to estimate the rate of the houses collapsed is feasible and effective.