Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

International Journal of Computational Intelligence and Applications

Volumn 16, Issue 1, 2017, Pages

  Yu, Long ^a   Wang, Zhiyin ^a   Tian, Shengwei ^a   Ye, Feiyue ^b   Ding, Jianli ^a   Kong, Jun ^c  

^a XINJIANG UNIVERSITY   (China)

^b JIANGSU UNIVERSITY OF TECHNOLOGY   (China)

^c JIANGNAN UNIVERSITY   (China)

Author keywords

convolutional neural networks; deep learning; landsat imagery; Water body extraction

Indexed keywords

CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; LEARNING SYSTEMS; NEURAL NETWORKS; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES;

CLASSIFICATION PERFORMANCE; CONVOLUTIONAL NEURAL NETWORK; LANDSAT IMAGERY; LEARNING FRAMEWORKS; LOGISTIC REGRESSIONS; MACHINE LEARNING METHODS; SPATIAL INFORMATIONS; WATERBODIES;

EXTRACTION;

EID: 85016204952     PISSN: 14690268     EISSN: None     Source Type: Journal    
DOI: 10.1142/S1469026817500018     Document Type: Article

References (29)

1. H. F. Chen, J. L. Wang, Z. Chen, L. Yang and W. J. Xi, Comparison of water extraction methods in mountainous plateau region from TM image, Remote Sens. Technol. Appl. 19(6) (2004) 479-484.
2. M. H. Zhang, Extracting water body information with improved model of spectral relationship in a higher mountain area, Geogr. Geo-Inf. Sci. 24(2) (2008) 14-16.
3. S. W. Yang, C. S. Xue, T. Liu and Y. K. Li, A method of small water information automatic extraction from TM remote sensing images, Acta Geod. Cartogr. Sin. 39(6) (2010) 611-617.
4. S. K. McFeeters, The use of the normalized di® erence water index (NDWI) in the delineation of open water feature, Int. J. Remote Sens. 17(7) (1996) 1425-1432.
5. H. Q. Xu, A study on information extraction of water body with the modīed normalized di® erence water index(MNDWI), J. Romote Sensing 9(5) (2005) 589-595.
6. J. B. Chen, S. X. Liu and C. Y. Wang, Research on urban water body extraction using knowledge-based decision tree, Remote Sens. Inf. 28(1) (2013) 29-33.
7. J. J. Li, The accurate extraction of frozen lake water based on SVM-neighboring lter assimilation model, Xi'an University of Science and Technology (2009) 1-40.
8. W. L. Yang, M. H. Yang and H. X. Qi, Water body extracting from TM image based on BPNN, Sci. Surv. Map. 37(1) (2012) 148-150.
9. P. S. Frazier and K. J. Page. Water body detection and delineation with Landsat TM data, Photogramm. Eng. Remote Sens. 66(12) (2000) 1461-1468.
10. G. E. Hinton and R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313(5786) (2006) 504-507.
11. K. Yu, L. Jia, Y. Chen and W. Xu, Deep learning: Yesterday, today, tomorrow, J. Comput. Res. Dev. 50(9) (2013) 1799-1804.
12. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa, Natural language processing (almost) from scratch, J. Mach. Learn. Res. 12 (2011) 2493-2537.
13. R. Socher, C. Y. Lin, A. Y. Ng and C. D. Manning, Parsing natural scenes and natural language with recursive neural networks, in Proc. of the International Conference on Machine Learning, Bellevue, Washington, USA (2011), pp. 129-136.
14. X. Y. Chen, S. M. Xiang, C. L. Liu and C. H. Pan, Vehicle detection in satellite images by hybrid deep convolutional neural networks, IEEE Geosci. Remote Sens. Lett. 11(10) (2014) 1797-1801.
15. Q. Lü, Y. Dou, X. Niu, J. Q. Xu and F. Xia, Remote sensing image classīcation based on DBN model, J. Comput. Res. Dev. 51(9) (2014) 1911-1918.
16. Y. S. Chen, Z. H. Lin, X. Zhao, G. Wang and Y. F. Gu, Deep learning-based classīcation of hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6) (2014) 2094-2107.
17. D. H. Wiesel and T. N. Hubel, Receptive elds of single neurones inthe cats striate cortex, J. Physiol. 148(3) (1959) 574-591.
18. Y. LeCun, L. Bottou, Y. Bengio and P. Haner, Gradient-based learning applied to document recognition, Proc. IEEE 86(11) (1998) 2278-2324.
19. Y. LeCun, F. J. Huang and L. Bottou, Learning methods for generic object recognition with invariance to pose and lighting, in Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2 (2004), pp. II-97.
20. N. Jaitly, P. Nguyen, A. Nguyen and V. Vanhoucke, Application of pretrained deep neural networks to large vocabulary speech recognition, in Proc. Interspeech, Grenoble, France: International Speech Communication Association (2012).
21. O. Abdel-Hamid, A. Mohamed, H. Jiang and G. Penn, Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition, in Proc. of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2012), pp. 4277-4280.
22. A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classīcation with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097-1105.
23. T. Wang, D. J. Wu, A. Coates and A. Y. Ng, End-to-end text recognition with convolutional neural networks, in Proc. of the Conference on Pattern Recognition (ICPR) (2012), pp. 3304-3308.
24. M. Szarvas, A. Yoshizawa, M. Yamamoto and J. Ogata, Pedestrian detection with convolutional neural networks, in Proc. of the IEEE Intelligent Vehicles Symposium (2005), pp. 224-229.
25. J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, in Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR) (2015), pp. 3431-3440.
26. W. Hu, Y. Y. Huang, W. Li, F. Zhang and H. C. Li, Deep convolutional neural networks for hyperspectral image classīcation, J. Sensors 2015 (2015) 1-12.
27. J. Yue, W. Z. Zhao, S. J. Mao and H. Liu, Spectral-spatial classīcation of hyperspectral images using deep convolutional neural networks, Remote Sens. Lett. 6(6) (2015) 468-477.
28. T. N. Sainath, A. R. Mohamed, B. Kingsbury and B. Ramabhadran, Deep convolutional neural networks for lvcsr, in Proc. of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013), pp. 8614-8618.
29. H. S. Li, R. Zhao and X. G. Wang, Highly e±cient forward and backward propagation of convolutional neural networks for pixelwise classīcation, arXiv preprint, arXiv: 1412. 4256 (2014).