myref

1.  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, 2015.
2.  S. Muruganandham, “Semantic Segmentation of Satellite Images using Deep Learning,” Master’s Thesis, 2016.
3.  S. Saito, T. Yamashita and Y. Aoki, “Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks,” Journal of Imaging Science and Technology, vol. 60, no. 1, pp. 1–9, 2016.
4.  J. Long, E. Shelhamer, and T. Darrell. “Fully Convolutional Networks for Semantic Segmentation,” In CVPR, 2015.
5.  H. Noh, S. Hong, and B. Han, “Learning Deconvolution Network for Semantic Segmentation,” In ICCV, 2015.
6.  V. Badrinarayanan, A. Handa, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling,” arxiv 2015.
7.  Volodymyr Mnih. “Machine Learning for Aerial Image Labeling,” PhD thesis. University of Toronto, 2013.
8.  R. Maurya, P. R. Gupta, and A. S. Shukla, “Road Extraction Using K-Means Clustering and Morphological Operations,” In ICIIP, 2011.
9.  V. Badrinarayanan, A. Kendall, R. Cipolla, and S. Member, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” arxiv 2016.
10. Clevert et al, “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs),” In ICLR, 2016.
11. G. Xu, D. Zhang and X. Liu, "Road Extraction in High Resolution Images from Google Earth," 2009, In ICICS, 2009.
12. F. Visin, M. Ciccone, and A. Romero, “ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation,” arxiv 2015.
13. M. Volpi and V. Ferrari, "Semantic Segmentation of Urban Scenes by Learning Local Class Interactions," In CVPRW, 2015.
14. J. Liu, B. Liu, and H. Lu, “Detection guided deconvolutional network for hierarchical feature learning,” Pattern Recognition., vol. 48, no. 8, pp. 2645–2655, 2015.
15. S. Hong, H. Noh, and B. Han, “Decoupled deep neural network for semi-supervised semantic segmentation,” in NIPS, pp. 1495–1503, 2015.
16. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical.” arxiv 2015.
17. K. Mcgarigal, “Landscape metrics for categorical map patterns,” vol. 2001, no. Chapter 5, pp. 1–77, 2001.
18. C. Poullis, “Tensor-Cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images,” ISPRS J. Photogramm. Remote Sens., vol. 95, pp. 93–108, 2014.
19. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by ReducingInternal Covariate Shif," International Conference on Machine Learning, 2015.
20. Simonyan, K. and Zisserman, A., "Very Deep Convolutional Networks for Large-Scale Image Recognition", In ICLR, 2015.
21. M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision, vol. 111, no. 1, pp. 98–136, 2015.
22. X. Huang and L. Zhang, “Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines,” Int. J. Remote Sens., vol. 30, no. 8, pp. 1977–1987, 2009.
23. T. Panboonyuen, P. Vateekul and K. Jitkajornwanich, “Image Vectorization of Road Satellite Data Sets,” Journal of Remote Sensing and GIS Association of Thailand, Vol.17 (ISSN 1513-4261), Special Issue (Page 310-321), 2016.
24. C. Sujatha and D. Selvathi, “Connected component-based technique for automatic extraction of road centerline in high resolution satellite images,” EURASIP J. Image Video Process., vol. 2015, no. 1, p. 8, 2015.
25. D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, and M. Datcu, “Classification with an edge: improving semantic image segmentation with boundary detection.” In CVPR, 2014.
26. D. P. Kingma, “Adam: A Method for Stochastic Optimization,” In ICLR, 2015.
27. L. Chen, G. Papandreou, S. Member, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” arXiv 2016.
28. L. Chen, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,” In ICLR, 2015.
29. E. Ahmed, S. Cohen, and B. Price, “Semantic Object Selection,” In CVPR, 2014.
30. A. Kendall, “Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding,” arXiv 2016.
31. A. Kalinovsky, A. Kalinovsky, and V. Kovalev, “Lung Image Segmentation Using Deep Learning Methods and Convolutional Neural Networks,” International Conference on Pattern Recognition and Information Processing, 2016.
32. Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang, “Deep Learning Markov Random Field for Semantic Segmentation,” arXiv 2016.
33. NFBIA Summer School, "Deep Learning Slide," 2015.
34. Michael Jamroz & Matthew Opala, "Deep Learning for Image Segmentation Slide," 2016.
35. Fei-Fei Li & Andrej Karpathy & Justin Johnson, "Deep Learning Lecture," 2016.
36. Yu Huang, "Visual Detection, Recognition and Tracking with Deep Learning Slide," 2016.
37. Ujjwalkarn, "An Intuitive Explanation of Convolutional Neural Networks," the data science blog, 2016.
38. Thanabhat Koomsubha, "Text Categorization for Thai Corpus Using Character-Level Convolutional Neural Netwok," Master’s Thesis, 2016.
39. Itsara Wichakam, "Breast Cancer Detection in Tomosynthesis Imaging Using Deep Convolutional Network," Master’s Thesis, 2016.