Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

Procedia Computer Science

Volumn 91, Issue , 2016, Pages 566-575

  Liu, Ying ^a,b   Wu, Linzhi ^a  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

deep learning; remote sensing image; target recognition

Indexed keywords

DISASTER PREVENTION; DISASTERS; GEOLOGY; IMAGE RECONSTRUCTION; LANDSLIDES;

DEEP LEARNING; DISASTER MONITORING; GEOLOGICAL DISASTER; OPTICAL REMOTE SENSING; PRE-PROCESSING METHOD; REMOTE SENSING IMAGES; TARGET RECOGNITION; WAVELET TRANSFORMATIONS;

REMOTE SENSING;

EID: 84984939548     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2016.07.144     Document Type: Conference Paper

References (31)

1. China geological disaster bulletin. http://www.cgs.gov.cn/xwtzgg/jrgengxin/29092.htm; 2014.
2. Zhang Ning. Study of Landslide disasters Evaluation Based on Multi-source Remote Sensing Data. Central South University; 2009.
3. Zhou Pinggen Indicator System and Techniques of Landslide Monitoring Journal of Geomechanics 10 1 2004 19 26
4. A. Wang Zhihua Review and the Prospects of Applying the Remote Sensing Technique to Oasis Study in China Remote Sensing for Land & Resources 1 2007 10 15
5. Yang Erhong, Wang Zhihua, and Jia Shaohui The Application of Remote Sensing Digital Technique in the Investigate of the Tiantai Landslide in Xuanhan County Journal of Jilin University (Earth Science Edition) 37 3 2007 557 563
6. Wang Zhihua Remote Sensing for Landslides in China Remote Sensing for Land & Resources 1 2005 1 7
7. Sun Zhijun, Xue Lei, Xu Yangming, and Wang Zheng Review of Deep Learning Application Research of Computers 29 8 2012 2806 2810
8. G.E. Dahl, D. Yu, L. Deng, and et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognitiuon Audio, Speech, and Language Processing, IEEE Transactions on 20 1 2012 30 42
9. Krizhevsky A, Sutskever I and Hinton G. ImageNet Classification with deep convolutional neural networks. NIPS' 2012.
10. Sun Y, Wang X, Tang X. Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE; 2014, p. 1891-1898.
11. Taigman Y, Yang M, Ranzato MA, et al. Deepface: closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE; 2014, p. 1701-1708.
12. Sun Y, Wang X, Tang X. Deep learning face representation by joint identification-verification. CoRR; 2014.
13. K. Thyagarajan Discrete Wavelet Transform China Proceedings of The 2005 International Conference; 1708 3 2005 193 197
14. G.E. Hinton, S. Osinder, and Y.W. The A Fast Learning Algorithm for Deep Belief Nets Neural Computation 18 7 2006 1527 1554
15. F. Mantovani, R. Soeters, and C.J. Western Remote Sensing Techniques for Landslide Studies and Disaster Zonation in Europe Geomorphology 15 3-4 1996 213 225
16. Wang Zhihua Reviewing and Prospecting for Applying Remote Sensing to Landslide and Debrisflow Investigation Remote Sensing for Land & Resources 41 3 1999 10 15
17. J. Barlow, Y. Martin, and S.E. Franlin Detecting trans lational lands lide scars using segmentation of Landsat ETM+and DEM data in the northern Cas-cade Mountains, British Columbia Canadian journ al of remote sensing; 29 2003 510 517
18. Y.E. Martin, and S.E. Franklin Classification of soil-and bed rock-dominated landlsides in British Colunbia using segmentation of satellite imagery and DEM data International journal of remote sensing 26 7 2005 1505 1509
19. Guan Ningning, Yin Jingyuan, Li Chengfan, Lei Ming, and Ming. Zhang Landslide Recignition in Remote Sensing Image Based on Fuzzy Support Vector Machine IEEE International Conference on Computer & Information Technology 2012 1103 1108
20. S. Lee, J.H. Ryu, K. Min, and J.S. Won Landslide susceptibility analysis using GIS and artificial neural network Proceedings of SPIE-The International Society for Optical Engineering 6749 8 2007 2719 2729
21. Song Ruhua, D. Hiromu, and Abe Kazutoki Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan International Journal of Sediment Research 23 2 2008 106 118
22. Ana Colubi, and et al. Favorability functions based on kernel density estimation for logistic models: A case study Computational Statistics and Data Analysis 52 9 2008 4533 4543
23. S. Lee Landslide susceptibility mapping using an artificial neural network in the Gangneung area, Korea International Journal of Remote Sensing 28 21 2007 4763 4783
24. X. Yao, L.G. Tham, and F.C. Dai Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong China. Geomorphology; 101 4 2008 572 582
25. Qin Shengjun, and Lin Zhiping Research of Unbalance Sentiment Classification Based on Denoising Autoencoders Science Technology and Engineering; 14 12 2014 232 235
26. M. Tello, and et al. A novel algorithm for ship detection in SAR imagery based on the wavelet transform IEEE Geosci. Remote Sens. Lett 2005 201 205
27. S. Mallat, and et al. Singularity Detection and Processing with Wavelets IEEE Trans. Inf 1992 617 643
28. P. Vincent, H. Larochelle, Y. Bengio, and P.A. Manzagol Exteacting and composing robust features with denoising autoencoders Machine Learning, Twenty-fifth International Conference, Helsinki, Finland June 2008 1096 1103
29. DeepLearnToolbox. http://github.com/happynear/DeepLearnToolbox.
30. Y. Bengio Learning Deep Architectures for AI Foundations & Trends in Machine Learning; 2 1 2009 1 127
31. Lin C J. LIBSVM: A library for support vector machines [OL]. http://www.csie.ntu.edu.tw/∼cjlin/libsvm/.