과제정보
This research was funded by the project for Joint Demand Technology R&D of Regional SMEs funded by the Korea Ministry of SMEs and Startups in 2023(Project No. RS-2023-00207672).
참고문헌
- A. Boguszewski, D. Batorski, N. Ziemba-Jankowska, T. Dziedzic, and A. Zambrzycka, "LandCover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June, 2021, Pages 1102, 1110., 2022. https://doi.org/10.48550/ arXiv.2005.02264
- F. Gerard et al., "Land cover change in Europe between 1950 and 2000 determined employing aerial photography," Progress in Physical Geography: Earth and Environment, Vol.34, No.2, pp.183-205, 2010. https://doi.org/10.1177/0309133309360141
- S. M. Azimi, E. Vig, R. Bahmanyar, M. Korner, and P. Reinartz, "Towards multi-class object detection in unconstrained remote sensing imagery," In Asian Conference on Computer Vision, Springer, pp.150-165, 2018.
- W. Zhou, G. Huang, and M. L. Cadenasso, "Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes," Landscape and Urban Planning, Vol.102, No.1, pp.54-63, 2011. https://doi.org/10.1016/j.landurbplan.2011.03.009
- A. Gupta, S. Watson, and H. Yin, "Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment," Neurocomputing, Vol.439, pp.22-33, 2021. https://doi.org/10.1016/j.neucom.2020.02.139
- J. Kim, S. Song, and S.-C. Yu, "Denoising Auto-encoder based image enhancement for high resolution sonar image," 2017 IEEE Underwater Technology (UT).
- Q. Xiang and X. Pang, "Improved Denoising Auto-encoders for image denoising," 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2018).
- V. Badrinarayanan, A. Kendall, and R. Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for image segmentation," arXiv preprint arXiv:1511.00561, 2015.
- O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation," Medical Image Computing and Computer-Assisted Intervention - MICCAI, pp.234-241, 2015.
- G. Sethi, B. S. Saini, and D. Singh, "Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method," Computers & Electrical Engineering, Vol.53, pp.244-262, 2016. https://doi.org/10.1016/j.compeleceng.2015.06.025.
- K. Wu and D. Zhang, "Robust tongue segmentation by fusing region-based and edge-based approaches," Expert Systems with Applications, Vol.42, No.21, pp.8027-8038, 2015. https://doi.org/10.1016/j.eswa.2015.06.032.
- P. Vijay and N. C. Patil, "Gray scale image segmentation using OTSU thresholding optimal approach," Journal for Research, Vol.2, No.5, pp.20-24, 2016.
- S. Aja-Fernandez, A. H. Curiale, and G. Vegas-SanchezFerrero, "A local fuzzy thresholding methodology for multi-region image segmentation," Knowledge-Based Systems, Vol.83, pp.1-12, 2015. https://doi.org/10.1016/j.knosys.2015.02.029.
- N. M. Zaitoun and M. J. Aqel. "Survey on image segmentation techniques," Procedia Computer Science, Vol.65, pp.797-806, 2015. https://doi.org/10.1016/j.procs.2015.09.027.
- S. Niu, Q. Chen, L. de Sisternes, Z. Ji, Z. Zhou, and D. L. Rubin, "Robust noise region-based active contour model via local similarity factor for image segmentation," Pattern Recognition, Vol.61, pp.104-119, 2017. https://doi.org/10.1016/j.patcog.2016.07.022.
- E. Anjna and E. R. Kaur. "Review of image segmentation technique," International Journal, Vol.8, No.4, pp.36-39, 2017.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
- J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in CVPR, pp.3431-3440, 2015.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Ad-vances in Neural Information Processing Systems, 25, pp.1097-1105, 2012.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.
- H. Noh, S. Hong, and B. Han, "Learning deconvolution network for semantic segmentation," in Proceedings of the IEEE International Conference on Computer Vision, pp.1520-1528, 2015.
- A. Chaurasia and E. Culurciello, "LinkNet: Exploiting encoder representations for efficient semantic segmentation," IEEE Visual Communications and Image Processing (VCIP), 2017.
- W. Liu, A. Rabinovich, and A. C. Berg, "ParseNet: Looking wider to see better, computer vision and pattern recognition," 2016. https://doi.org/10.48550/arXiv.1506.04579
- T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2117-2125, 2017.
- M. L. L. de Oliveira and M. J. G. Bekooij, "Deep convolutional autoencoder applied for noise reduction in range-doppler maps of FMCW radars," 2020 IEEE International Radar Conference (RADAR).
- S. Park, S. Yu, M. Kim, K. Park, and J. Paik, "Dual autoencoder network for retinex-based low-light image enhancement," IEEE Access (Volume: 6), 2018.
- P. Schuch, S. Schulz, and C. Busch, "De-convolutional auto-encoder for enhancement of fingerprint samples," Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2016.
- Satellite images of Dubai Dataset [Internet], https://www.kaggle.com/datasets/humansintheloop/semantic-segmentation-of-aerial-imagery
- Abien Fred Agarap, Deep Learning using Rectified Linear Units (ReLU), 2019. https://doi.org/10.48550/arXiv.1803.08375
- 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 (CVPR), pp.770-778, 2016.
- D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," 3rd International Conference for Learning Representations, San Diego, 2015.