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| Automatic recognition of damaged town buildings caused by earthquake using remote sensing information: Taking the 2001 Bhuj, India, earthquake and the 1976 Tangshan, China, earthquake as examples | |
| 作者姓名： | 柳稼航 单新建 尹京苑 |
| 作者单位： | Institute of Geology，China Earthquake Administration，Beijing 100029，China，Xi′an Institute of Optics and Precision Mechanics，Chinese Academy of Sciences，Xi′an 710068，China，Institute of Geology，China Earthquake Administration，Beijing 100029，China，Earthquake Administration of Shanghai，Shanghai 200062，China |
| 基金项目： | National Public Welfare Research Program of Shanghai Technological Committee (2001BA601B-04-01-05) and Na- tional High Technology Research & Development Program of China (2001AA136040) during the tenth Five-year Plan. |
| 摘 要： | Introduction As we well know, the hazard of earthquake is very wide especially in cities. The conventionalmethods to investigate the damage are difficult to meet the requirements in applications. In recentyears, with the rapid development of remote sensing, especially the successful launch and applica-tion of high-resolution commercial remote sensing satellite, it has become possible to recognize andextract damage information by using remote sensing. The researchers at home and abroad hav… |
| 收稿时间： | 19 March 2003 |
| 修稿时间： | 25 August 2003 |
Automatic recognition of damaged town buildings caused by earthquake using remote sensing information: Taking the 2001 Bhuj, India, earthquake and the 1976 Tangshan, China, earthquake as examples |
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| Liu Jia-hang , Shan Xin-jian and Yin Jing-yuan.Automatic recognition of damaged town buildings caused by earthquake using remote sensing information: Taking the 2001 Bhuj, India, earthquake and the 1976 Tangshan, China, earthquake as examples[J].Acta Seismologica Sinica(English Edition),2004,17(6):686-696. | |
| Authors: | Liu Jia-hang Shan Xin-jian and Yin Jing-yuan |
| Institution: | 1. Institute of Geology, China Earthquake Administration, Beijing 100029, China;Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710068, China 2. Institute of Geology, China Earthquake Administration, Beijing 100029, China 3. Earthquake Administration of Shanghai, Shanghai 200062, China |
| Abstract: | In the high-resolution images, the undamaged buildings generally show a natural textural feature, while the dam- aged or semi-damaged buildings always exhibit some low-grayscale blocks because of their coarsely damaged sections. If we use a proper threshold to classify the grayscale of image, some independent holes will appear in the damaged regions. By using such statistical information as the number of holes in every region, or the ratio between the area of holes and that of the region, etc, the damaged buildings can be separated from the undamaged, thus automatic detection of damaged buildings can be realized. Based on these characteristics, a new method to auto- matically detect the damage buildings by using regional structure and statistical information of texture is presented in the paper. In order to test its validity, 1-m-resolution iKonos merged image of the 2001 Bhuj earthquake and grayscale aerial photos of the 1976 Tangshan earthquake are selected as two examples to automatically detect the damaged buildings. Satisfied results are obtained. |
| Keywords: | region analysis damage recognition image comprehension Bhujearthquake Tangshanearthquake |
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