太阳成8722(中国)有限公司-GREEN NO.1

标题: A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

作者: Xu, ZG (Xu, Zhigiang); Chen, YM (Chen, Yumin); Yang, F (Yang, Fan); Chu, TY (Chu, Tianyou); Zhou, HY (Zhou, Hongyan)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION  卷: 9  期: 4  文献号: 238  DOI: 10.3390/ijg19040238  出版年: APR 2020  

摘要: The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

入藏号: WOS:000539535700053

语言: English

文献类型: Article

作者关键词: earthquake disasters; scene recognition; deep learning; classical SSD method; transfer learning

地址: [Xu, Zhigiang; Chen, Yumin; Yang, Fan; Chu, Tianyou; Zhou, Hongyan] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址:

Wuhan University Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: xuzq97@whu.edu.cn; ymchen@whu.edu.cn; fan_yang@whu.edu.cn; chutianyou@whu.edu.cn; 2019282050143@whu.edu.cn

影响因子：2.239