1. [1] F. Rottensteiner, "Advanced methods for automated object extraction from LiDAR in urban areas," in Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, pp. 5402-5405, 2012. [ DOI:10.1109/IGARSS.2012.6352385] 2. [2] J. Schiewe, "Segmentation of high-resolution remotely sensed data-concepts, applications and problems," International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, vol. 34, pp. 380-385, 2002. 3. [3] F. Rottensteiner and C. Briese, Automatic generation of building models from LIDAR data and the integration of aerial images: na, 2003. [ DOI:10.1109/MCG.2003.1242381] 4. [4] F. Lafarge, X. Descombes, J. Zerubia, and M. Pierrot-Deseilligny, "Structural approach for building reconstruction from a single DSM," IEEE Transactions on pattern analysis and machine intelligence, vol. 32, pp. 135-147, 2010. [ DOI:10.1109/TPAMI.2008.281] 5. [5] I. V. Florinsky, "Combined analysis of digital terrain models and remotely sensed data in landscape investigations," Progress in Physical Geography, vol. 22, pp. 33-60, 1998. [ DOI:10.1177/030913339802200102] 6. [6] H. Murakami, K. Nakagawa, H. Hasegawa, T. Shibata, and E. Iwanami, "Change detection of buildings using an airborne laser scanner," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 54, pp. 148-152, 1999. [ DOI:10.1016/S0924-2716(99)00006-4] 7. [7] B. P. Olsen, T. Knudsen, and P. Frederiksen, "Digital change detection for map database update," International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, vol. 34, pp. 357-364, 2002. 8. [8] F. H. Sinz, J. Q. Candela, G. H. Bakır, C. E. Rasmussen, and M. O. Franz, "Learning depth from stereo," in Joint Pattern Recognition Symposium, pp. 245-252, 2004. [ DOI:10.1007/978-3-540-28649-3_30] 9. [9] J. Skilling and S. Gull, "Algorithms and applications," in Maximum-entropy and Bayesian methods in inverse problems, ed: Springer, pp. 83-132, 1985. [ DOI:10.1007/978-94-017-2221-6_5] 10. [10] D. Eigen and R. Fergus, "Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture," in Proceedings of the IEEE International Conference on Computer Vision, pp. 2650-2658, 2015. [ DOI:10.1109/ICCV.2015.304] 11. [11] D. Eigen, C. Puhrsch, and R. Fergus, "Depth map prediction from a single image using a multi-scale deep network," Advances in neural information processing systems, pp. 2366-2374, 2014. 12. [12] F. Liu, C. Shen, G. Lin, and I. Reid, "Learning depth from single monocular images using deep convolutional neural fields," IEEE transactions on pattern analysis and machine intelligence, vol. 38, pp. 2024-2039, 2016. [ DOI:10.1109/TPAMI.2015.2505283] 13. [13] A. Saxena, S. H. Chung, and A. Y. Ng, "3-d depth reconstruction from a single still image," International journal of computer vision, vol. 76, pp. 53-69, 2008. [ DOI:10.1007/s11263-007-0071-y] 14. [14] I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, "Deeper depth prediction with fully convolutional residual networks," 3D Vision (3DV), 2016 Fourth International Conference on, pp. 239-248, 2016. [ DOI:10.1109/3DV.2016.32] 15. [15] Z.-m. Yang and H.-d. Zhao, "A New RBF Reflection Model for Shape from Shading," 3D Research, vol. 8, p. 33, 2017. [ DOI:10.1007/s13319-017-0141-z] 16. [16] M. A. Rajabi and J. R. Blais, "Improvement of digital terrain model interpolation using SFS techniques with single satellite imagery," International Conference on Computational Science, pp. 164-173, 2002. [ DOI:10.1007/3-540-47789-6_17] 17. [17] M. A. Rajabi and J. R. Blais, "Optimization of DTM interpolation using SFS with single satellite imagery," The Journal of Supercomputing, vol. 28, pp. 193-213, 2004. [ DOI:10.1023/B:SUPE.0000020178.66165.f3] 18. [18] J. Schennings, "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation," ed, 2017. 19. [19] I. P. Howard, "Perceiving in depth, Vol. 3: Other mechanisms of depth perception," 2012. [ DOI:10.1093/acprof:oso/9780199764167.001.0001] 20. [20] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014. 21. [21] Z. Zhang, C. Xu, J. Yang, Y. Tai, and L. Chen, "Deep hierarchical guidance and regularization learning for end-to-end depth estimation," Pattern Recognition, vol. 83, pp. 430-442, 2018. [ DOI:10.1016/j.patcog.2018.05.016] 22. [22] B. Li, Y. Dai, and M. He, "Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference," Pattern Recognition, 2018. [ DOI:10.1016/j.patcog.2018.05.029] 23. [23] A. Saxena, S. H. Chung, and A. Y. Ng, "Learning depth from single monocular images," Advances in neural information processing systems, pp. 1161-1168, 2006. 24. [24] M. Liu, M. Salzmann, and X. He, "Discrete-continuous depth estimation from a single image," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 716-723, 2014. [ DOI:10.1109/CVPR.2014.97] 25. [25] S. Srivastava, M. Volpi, and D. Tuia, "Joint height estimation and semantic labeling of monocular aerial images with CNNs," Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, pp. 5173-5176, 2017. [ DOI:10.1109/IGARSS.2017.8128167] 26. [26] L. Mou and X. X. Zhu, "IM2HEIGHT: Height estimation from single monocular imagery via fully residual convolutional-deconvolutional network," arXiv preprint arXiv:1802.10249, 2018. 27. [27] P. Ghamisi and N. Yokoya, "IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net," IEEE Geoscience and Remote Sensing Letters, vol. 15, pp. 794-798, 2018. [ DOI:10.1109/LGRS.2018.2806945] 28. [28] H. A. Amirkolaee and H. Arefi, "Height estimation from single aerial images using a deep convolutional encoder-decoder network," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 149, pp. 50-66, 2019. [ DOI:10.1016/j.isprsjprs.2019.01.013] 29. [29] H. A. Amirkolaee and H. Arefi, "Convolutional neural network architecture for digital surface model estimation from single remote sensing image," Journal of Applied Remote Sensing, vol. 13, p. 016522, 2019. [ DOI:10.1117/1.JRS.13.016522] 30. [30] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez, "A review on deep learning techniques applied to semantic segmentation," arXiv preprint arXiv:1704.06857, 2017. [ DOI:10.1016/j.asoc.2018.05.018] 31. [31] M. Lin, Q. Chen, and S. Yan, "Network in network," arXiv preprint arXiv:1312.4400, 2013. 32. [32] Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," Proceedings of the 27th international conference on machine learning (ICML-10), pp. 111-118, 2010. 33. [33] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, pp. 1097-1105, 2012. 34. [34] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, 2015. [ DOI:10.1038/nature14539] 35. [35] R. H. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, and H. S. Seung, "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, vol. 405, p. 947, 2000. [ DOI:10.1038/35016072] 36. [36] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv:1502.03167, 2015. 37. [37] S. Wager, S. Wang, and P. S. Liang, "Dropout training as adaptive regularization," Advances in neural information processing systems, pp. 351-359, 2013. 38. [38] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016. [ DOI:10.1109/CVPR.2016.90] 39. [39] J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440, 2015. [ DOI:10.1109/CVPR.2015.7298965] 40. [40] H. Noh, S. Hong, and B. Han, "Learning deconvolution network for semantic segmentation," Proceedings of the IEEE international conference on computer vision, pp. 1520-1528, 2015. [ DOI:10.1109/ICCV.2015.178] 41. [41] A. Dosovitskiy, J. T. Springenberg, and T. Brox, "Learning to generate chairs with convolutional neural networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1538-154, 2015. [ DOI:10.1109/CVPR.2015.7298761] 42. [42] M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European conference on computer vision, pp. 818-833, 2014 [ DOI:10.1007/978-3-319-10590-1_53] 43. [43] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9 [ DOI:10.1109/CVPR.2015.7298594]
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