In order to improve the precision of damage assessment of post-earthquake buildings based on remote sensing images, this paper introduces a deep convolutional neural network (DCNN) model that performs well in natural image classification and target detection, and also proposes a method of using DCNN fully-connected layer features combined with support vector machine (SVM) to detect damaged building areas in remote sensing images. Firstly, neural network feed forward is used to extract the features of the training samples and the regions to be detected from the DCNN fully-connected layer; then the SVM classifier is learned based on the training samples; finally, all the blocks in the detection region are subjected to predicting and voting to determine whether they are damaged. The authors used Haiti earthquake remote sensing imagery in 2010 to do verification. The accuracy rate of damage detection in this method can reach 89%. Compared with the traditional feature extraction method, the correct rate is improved by 4%. The experimental results show that this method has a certain potential in the detection of building damage damage.
周阳,张云生,陈斯飏,邹峥嵘,朱耀晨,赵芮雪. 基于DCNN特征的建筑物震害损毁区域检测[J]. 国土资源遥感, 2019, 31(2): 44-50.
Yang ZHOU,Yunsheng ZHANG,Siyang CHEN,Zhengrong ZOU,Yaochen ZHU,Ruixue ZHAO. Disaster damage detection in building areas based on DCNN features. Remote Sensing for Land & Resources, 2019, 31(2): 44-50.
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