首页> 外文会议>Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International >Extracting damaged building information from single remote sensing images of post-earthquake
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Extracting damaged building information from single remote sensing images of post-earthquake

机译:从地震后的单个遥感影像中提取受损的建筑物信息

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In the high resolution images, undamaged buildings take on natural texture features, but to the damaged or extensive damaged buildings, there are always some low grayscale blocks because of their coarsely damaged section. By using such statistical information as the number of holes in every region, or the ratio between the area of the region and the holes', et al., damaged buildings can be separated from undamaged buildings, thus automatic detection of damaged buildings can be reached. Based on these characteristics, in this work, a new method to detect the damage buildings automatically by using region structure and statistic information of the texture is presented. Also, in order to test its validity, 1-meter-resolution iKonos merged image of Bhuj earthquake, India, 2001, and grayscale aerial photos of Tangshan earthquake, China, 1976, are selected as two examples to detect the damaged buildings automatically. Satisfactory results are obtained.
机译:在高分辨率图像中,未损坏的建筑物具有自然的纹理特征,但是对于受损或大面积受损的建筑物,由于它们的横截面较粗,总是会出现一些低灰度块。通过使用诸如每个区域中的孔的数量或该区域的面积与孔的比率之类的统计信息等,可以将损坏的建筑物与未损坏的建筑物分开,从而可以自动检测损坏的建筑物。基于这些特征,提出了一种利用区域结构和纹理统计信息自动检测受损建筑物的新方法。此外,为了测试其有效性,我们选择了1米分辨率的iKonos合并图像(2001年,印度普吉地震)和1976年中国唐山地震的灰度航拍照片作为自动检测受损建筑物的两个示例。获得令人满意的结果。

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