Label noise tolerance of deep semantic segmentation networks for extracting buildings in ultra-high-resolution aerial images of semi-built environments

References

• Adnan MSG, Rahman MS, Ahmed N, Ahmed B, Rabbi M, Rahman RM. 2020. Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sens. 12(20):3347.
   Google Scholar
• Ahmed B, Rahman MS, Sammonds P, Islam R, Uddin K. 2020a. Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh. Geomatics Nat Hazards Risk. 11(1):446–468.
   Web of Science ®Google Scholar
• Ahmed N, Mahbub RB, Rahman RM. 2020b. Learning to extract buildings from ultra-high-resolution drone images and noisy labels. Int J Remote Sens. 41(21):8216–8237.
   Web of Science ®Google Scholar
• Ahmed N, Firoze A, Rahman RM. 2020c. Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure. J Inf Telecommun. 4(2):175–198.
   Google Scholar
• Alam A, Sammonds P, Ahmed B. 2020. Cyclone risk assessment of the Cox’s Bazar district and Rohingya refugee camps in southeast Bangladesh. Sci Total Environ. 704:135360.
   PubMed Web of Science ®Google Scholar
• Bootkrajang J, Kabán A. 2012. Label-noise robust logistic regression and its applications. In: Joint European conference on machine learning and knowledge discovery in databases. Berlin, Heidelberg: Springer. p. 143–158.
   Google Scholar
• Chen Y, Tang L, Yang X, Bilal M, Li Q. 2020. Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery. Neurocomputing. 386:136–146.
   Web of Science ®Google Scholar
• Frank J, Rebbapragada U, Bialas J, Oommen T, Havens TC. 2017. Effect of label noise on the machine-learned classification of earthquake damage. Remote Sens. 9(8):803.
   Google Scholar
• Frénay B, Verleysen M. 2014. Classification in the presence of label noise: a survey. IEEE Trans Neural Netw Learning Syst. 25(5):845–869.
   PubMed Web of Science ®Google Scholar
• Garcia LP, de Carvalho AC, Lorena AC. 2015. Effect of label noise in the complexity of classification problems. Neurocomputing. 160:108–119.
   Web of Science ®Google Scholar
• Huang Z, Cheng G, Wang H, Li H, Shi L, Pan C. 2016. Building extraction from multi-source remote sensing images via deep deconvolution neural networks. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: Ieee. p. 1835–1838.
   Google Scholar
• Ji S, Wei S, Lu M. 2019. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Trans Geosci Remote Sens. 57(1):574–586.
   Web of Science ®Google Scholar
• Kingma DP, Ba J. 2014. Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR): Banff, Canada.
   Google Scholar
• Long J, Shelhamer E, Darrell T. 2015. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, Massachusetts: IEEE Xplore. p. 3431–3440.
   Google Scholar
• Maas AE, Rottensteiner F, Heipke C. 2019. A label noise tolerant random forest for the classification of remote sensing data based on outdated maps for training. Comput Vision Image Underst. 188:102782.
   Web of Science ®Google Scholar
• Maas A, Rottensteiner F, Heipke C. 2016. Using label noise robust logistic regression for automated updating of topographic geospatial databases. In: XXIII ISPRS Congress, Commission VII 3 (2016), Nr. 7. Vol. 3, No. 7. Göttingen: Copernicus GmbH. p. 133–140.
   Google Scholar
• Mnih V, Hinton GE. 2012. Learning to label aerial images from noisy data. In: Proceedings of the 29th International Conference on Machine Learning (ICML-12); Edinburgh, Scotland. p. 567–574.
   Google Scholar
• Pelletier C, Valero S, Inglada J, Champion N, Marais Sicre C, Dedieu G. 2017. Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens. 9(2):173.
   Google Scholar
• Rolnick D, Veit A, Belongie S, Shavit N. 2017. Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. 2015. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, Cham. p. 234–241.
   Google Scholar
• Saralioglu E, Gungor O. 2021. Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images. Geocarto Int. :1–20.
   Web of Science ®Google Scholar
• Xu Y, Wu L, Xie Z, Chen Z. 2018. Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens. 10(1):144.
   Google Scholar
• Yang H, Wu P, Yao X, Wu Y, Wang B, Xu Y. 2018. Building extraction in very high resolution imagery by dense-attention networks. Remote Sens. 10(11):1768.
   Google Scholar
• Yuan J. 2018. Learning building extraction in aerial scenes with convolutional networks. IEEE Trans Pattern Anal Mach Intell. 40(11):2793–2798.
   PubMed Web of Science ®Google Scholar
• Zhang Z, Guo W, Li M, Yu W. 2020. GIS-supervised building extraction with label noise-adaptive fully convolutional neural network. IEEE Geosci Remote Sens Lett. 17(12):2135–2139.
   Web of Science ®Google Scholar