Resnet-Unet considering Patches (RUP) network to solve the problem of patches due to shadows in extracting building top information

References

• Alsabhan, W., Alotaiby, T., and Chaudhary, G., 2022. Automatic building extraction on satellite images using Unet and ResNet50. Computational Intelligence and Neuroscience, 2022, 5008854. doi:10.1155/2022/5008854
   PubMed Web of Science ®Google Scholar
• Bittner, K., Cui, S., and Reinartz, P., 2017. Building extraction from remote sensing data using fully convolutional networks. In: ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hannover, Germany: Copernicus Publications, XLII-1/W1, 481–486.
   Google Scholar
• Cai, Y., et al., 2021. An automatic trough line identification method based on improved Unet. Atmospheric Research, 264 (4), 105839. doi:10.1016/j.atmosres.2021.105839
   Google Scholar
• Cao, D., et al., 2021. A stacking ensemble deep learning model for building extraction from remote sensing images. Remote Sensing, 13 (19), 3898. doi:10.3390/rs13193898
   Web of Science ®Google Scholar
• Chen, Y., et al., 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience & Remote Sensing, 54 (10), 6232–6251. doi:10.1109/TGRS.2016.2584107
   Web of Science ®Google Scholar
• Drăguţ, L., et al., 2014. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119–127. doi:10.1016/j.isprsjprs.2013.11.018
   PubMed Web of Science ®Google Scholar
• Fjellström, C. and Nyström, K., 2022. Deep learning, stochastic gradient descent and diffusion maps. Journal of Computational Mathematics and Data Science, 4, 100054.
   Google Scholar
• Fukushima, K. and Miyake, S., 1982. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition, 15 (6), 455–469. doi:10.1016/0031-3203(82)90024-3
   Web of Science ®Google Scholar
• Hosseinpour, H., Samadzadegan, F., and Javan, F.D., 2022. CMGFNet: a deep cross-modal gated fusion network for building extraction from very high-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 6–115. doi:10.1016/j.isprsjprs.2021.12.007
   Web of Science ®Google Scholar
• Jin, X. and Davis, C.H., 2005. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. Eurasip Journal on Advances in Signal Processing, 2005 (14), 2196–2206. doi:10.1155/ASP.2005.2196
   Web of Science ®Google Scholar
• Kingma, D.P. and Ba, J., 2014. Adam: a method for stochastic optimization. arXiv preprint, arXiv:1412.6980.
   Google Scholar
• Lecun, Y. and Bottou, L., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11), 2278–2324. doi:10.1109/5.726791
   Web of Science ®Google Scholar
• Li, C., et al., 2021. Attention enhanced U-Net for building extraction from farmland based on google and worldview-2 remote sensing images. Remote Sensing, 13 (21), 4411. doi:10.3390/rs13214411
   Web of Science ®Google Scholar
• Li, Y., et al., 2010. A refined marker controlled watershed for building extraction from DSM and imagery. International Journal of Remote Sensing, 31 (6), 1441–1452. doi:10.1080/01431160903475373
   Web of Science ®Google Scholar
• Li, Y., et al., 2022. SSDBN: a single-side dual-branch network with encoder-decoder for building extraction. Remote Sensing, 14 (3), 768. doi:10.3390/rs14030768
   Web of Science ®Google Scholar
• Li, L. and Liang, Y., 2021. Deep learning target vehicle detection method based on YOLOv3-tiny. In: 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China: IEEE, 1575–1579.
   Google Scholar
• Madey, A.S.A., Yahyaoui, A., and Rasheed, J., 2021. Object detection in video by detecting vehicles using machine learning and deep learning approaches. In: 2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era (FoNeS-AIoT), Nicosia, Turkey: IEEE, 62–65.
   Google Scholar
• Mnih, V., 2013. Machine Learning for Aerial Image Labeling. Canada: University of Toronto.
   Google Scholar
• Nyaruhuma, A.P., et al., 2012. Verification of 2D building outlines using oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 62–75. doi:10.1016/j.isprsjprs.2012.04.007
   Web of Science ®Google Scholar
• Ok, A.O., 2013. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry and Remote Sensing, 86 (12), 21–40. doi:10.1016/j.isprsjprs.2013.09.004
   Google Scholar
• Ranjan, A. and Machavaram, R., 2022. Detection and localisation of farm mangoes using YOLOv5 deep learning technique. In: 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai, India: IEEE, 1–5.
   Google Scholar
• Salberg, A.B., 2015. Detection of seals in remote sensing images using features extracted from deep convolutional neural networks. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy: IEEE, 1893–1896.
   Google Scholar
• Singhal, S. and Radhika, S., 2014. Automatic detection of buildings from aerial images using color invariant features and canny edge detection. International Journal of Engineering Trends and Technology (IJETT), 11 (8), 393–396. doi:10.14445/22315381/IJETT-V11P277
   Google Scholar
• Sun, L., Tang, Y., and Zhang, L., 2017. Rural building detection in high-resolution imagery based on a two-stage CNN model. IEEE Geoscience and Remote Sensing Letters, 14 (11), 1998–2002. doi:10.1109/LGRS.2017.2745900
   Web of Science ®Google Scholar
• Temenos, A., et al., 2022. On the exploration of automatic building extraction from RGB satellite images using deep learning architectures based on U-Net. Technologies, 10 (1), 19. doi:10.3390/technologies10010019
   Web of Science ®Google Scholar
• Wang, Y., et al., 2022. B-FGC-Net: a building extraction network from high resolution remote sensing imagery. Remote Sensing, 14 (2), 269. doi:10.3390/rs14020269
   Web of Science ®Google Scholar
• Yang, H.L., et al., 2018. Building extraction at scale using convolutional neural network: mapping of the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (8), 1–15. doi:10.1109/JSTARS.2018.2835377
   Web of Science ®Google Scholar
• Yang, X. and Qiao, Y., 2022. Infrared long-distance target detection based on deep learning. In: 2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI), Xiamen, China: IEEE, 1–5.
   Google Scholar
• Zhao, Y., et al., 2019. Robust real-time object detection based on deep learning for very high resolution remote sensing images. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan: IEEE, 1314–1317.
   Google Scholar