Development of automated extraction method for building damage area based on maximum likelihood classifier
Development of automated extraction method for building damage area based on maximum likelihood classifier
复制标题
基于最大似然分类器的建筑受损区域自动提取方法开发
DOI：
--
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Matsuoka
中科院分区：
文献类型：
--
作者：
H. Mitomi;F. Yamazaki;M. Matsuoka
利用近期地震后的直升机图像，采用多元正态分布的最大似然度分类器对图像中的部分训练数据进行统计，研究了一种严重受损建筑物类别的自动提取方法。在图像处理中，利用色调、饱和度、亮度、边缘强度和边缘强度方差来提取受损建筑物的信息，而不需要像Hasegawa等（2000b）的方法那样，根据经验确定每张图像的阈值。该方法对大多数类别的分类结果都很好。其中，倒塌建筑的分类与建筑的实际损坏情况吻合较好。因此，如果我们能够在每个班级中建立一些训练数据，我们可以快速获得建筑物受损区域的满意结果，并有望将该方法应用于实时地震灾害管理。
Using some images taken from a helicopter after recent earthquakes, an automatic extraction method of the class of severely damaged buildings was examined by the maximum like- lihood classifier with a multivariate normal distribution for the statistics of some training data in images. In the image processing, hue, saturation, brightness, edge intensity and variance of edge in- tensity were used for the extraction of information on damaged buildings, and the threshold value for each image did not have to be decided empirically such as in the method of Hasegawa et al. (2000b). The classification result was good for most of the classes used by this method. In particu- lar, the class denoting collapsed buildings agreed well with the actual situation of building damage. Therefore, if we could set up some training data in each class, we would obtain favorite results for areas with building damage rapidly, and the application of this method to real-time earthquake dis- aster management can be expected.