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Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning
GUO Jiawei1,2, LI Yongshu1, WANG Hongshu3, LU Heng4,5, WANG Xiaobo6,7
1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;2.Information Technology Center of Chengdu Planning and Management Bureau, Chengdu 610094, China;3.Department of Surveying and Mapping Engineering, Sichuan Water Conservancy Vocational College, Chengdu 611231, China;4.State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China;5.College of Hydraulic and Hydroelectric Engineering, Sichuan University, Chengdu 610065, China;6.Provincial Geomatics Center of Qinghai, Xining 810001, China;7.Geomatics Technology and Application Key Laboratory of Qinghai Province, Xining 810001, China
摘要:
A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.
关键词:  Earthquake  Debris flow  UAV high-resolution image  Transfer learning  Information detection
DOI:10.19743/j.cnki.0891-4176.201901013
分类号:
基金项目:This research was supported by the National Natural Science Foundation of China (41701499),the Sichuan Science and Technology Program (2018GZ0265),and the Geomatics Technology and Application Key Laboratory of Qinghai Province (QHDX-2018-07).
Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning
GUO Jiawei1,2, LI Yongshu1, WANG Hongshu3, LU Heng4,5, WANG Xiaobo6,7
1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;2.Information Technology Center of Chengdu Planning and Management Bureau, Chengdu 610094, China;3.Department of Surveying and Mapping Engineering, Sichuan Water Conservancy Vocational College, Chengdu 611231, China;4.State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China;5.College of Hydraulic and Hydroelectric Engineering, Sichuan University, Chengdu 610065, China;6.Provincial Geomatics Center of Qinghai, Xining 810001, China;7.Geomatics Technology and Application Key Laboratory of Qinghai Province, Xining 810001, China
Abstract:
A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.
Key words:  Earthquake  Debris flow  UAV high-resolution image  Transfer learning  Information detection