River body extraction using convolutional neural network

References

• L. Yu, Z. Wang, S. Tian, F. Ye, J. Ding, and J. Kong, “Convolutional neural networks for water body extraction from landsat imagery,” International Journal of Computational Intelligence and Applications, vol. 16, no. 01, p. 1750001, 2017.
   Google Scholar
• H. Chen, J. Wang, Z. Chen, L. Yang, and W. Xi, “Comparison of water extraction methods in mountainous plateau region from tm image,” Remote Sensing Technology and Application, vol. 19, no. 6, pp. 479–484, 2004.
   Google Scholar
• M.-h. Zhang, “Extracting water-body information with improved model of spectal relationship in a higher mountain area [j],” Geography and Geo-Information Science, vol. 2, 2008.
   Google Scholar
• S. K. McFeeters, “The use of the normalized difference water index (ndwi) in the delineation of open water features,” International journal of remote sensing, vol. 17, no. 7, pp. 1425–1432, 1996. doi: 10.1080/01431169608948714
   Web of Science ®Google Scholar
• D. M. Miller, E. J. Kaminsky, and S. Rana, “Neural network classification of remote-sensing data,” Computers & Geosciences, vol. 21, no. 3, pp. 377–386, 1995. doi: 10.1016/0098-3004(94)00082-6
   Web of Science ®Google Scholar
• M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, “Spectral and spatial classification of hyperspectral data using svms and morphological profiles,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 11, pp. 3804–3814, 2008. doi: 10.1109/TGRS.2008.922034
   Web of Science ®Google Scholar
• M. Belgiu and L. Draguţ, “Random forest in remotĕ sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, 2016. doi: 10.1016/j.isprsjprs.2016.01.011
   Web of Science ®Google Scholar
• S. Achar, B. Sankaran, S. Nuske, S. Scherer, and S. Singh, “Self-supervised segmentation of river scenes,” in 2011 IEEE International Conference on Robotics and Automation. IEEE, 2011, pp. 6227–6232.
   Google Scholar
• Y. Yuan, J. Lin, and Q. Wang, “Hyperspectral image classification via multitask joint sparse representation and stepwise mrf optimization,” IEEE transactions on cybernetics, vol. 46, no. 12, pp. 2966–2977, 2016. doi: 10.1109/TCYB.2015.2484324
   PubMed Web of Science ®Google Scholar
• J. Wang, J. Song, M. Chen, and Z. Yang, “Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine,” International Journal of Remote Sensing, vol. 36, no. 12, pp. 3144–3169, 2015. doi: 10.1080/01431161.2015.1054049
   Web of Science ®Google Scholar
• H. Patel, A. Thakkar, M. Pandya and K. Makwana (2018) Neural network with deep learning architectures, Journal of Information and Optimization Sciences, 39:1, 31-38, DOI: 10.1080/02522667.2017.1372908
   Web of Science ®Google Scholar
• A. K. Dubey and V. Jain (2019) A review of face recognition methods using deep learning network, Journal of Information and Optimization Sciences, 40:2, 547-558, DOI: 10.1080/02522667.2019.1582875
   Web of Science ®Google Scholar
• Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278– 2324, 1998. doi: 10.1109/5.726791
   Web of Science ®Google Scholar
• Y. LeCun, F. J. Huang, and L. Bottou, “Learning methods for generic object recognition with invariance to pose and lighting,” in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 2. IEEE, 2004, pp.–104.
   Google Scholar
• H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1520–1528.
   Google Scholar
• V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” arXiv preprint arXiv:1511.00561, 2015.
   Google Scholar
• D. Scherer, A. Muller, and S. Behnke, “Evaluation of pooling operations in convolutional architectures for object recognition,” in Artificial Neural Networks–ICANN 2010. Springer, 2010, pp. 92–101.
   Google Scholar
• L. Lopez-Fuentes, C. Rossi, and H. Skinnemoen, “River segmentation for flood monitoring,” in Big Data (Big Data), 2017 IEEE International Conference on. IEEE, 2017, pp. 3746–3749.
   Google Scholar