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Original Articles

An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images

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Pages 3355-3370 | Received 06 Jul 2020, Accepted 08 Nov 2020, Published online: 26 Dec 2020

References

  • Abdollahi A, Bakhtiari HRR, Nejad MP. 2018. Investigation of svm and level set interactive methods for road extraction from google earth images. J Indian Soc Remote Sens. 46(3):423–430.
  • Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A. 2020. Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens. 12(9):1444.
  • Alshehhi R, Marpu PR, Woon WL, Dalla Mura M. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens. 130:139–149.
  • Audebert N, Boulch A, Lagrange A, Le Saux B, Lefevre S. 2016. Deep learning for remote sensing. Technical Report.
  • Audebert N, Boulch A, Randrianarivo H, Le Saux B, Ferecatu M, Lefèvre S, Marlet R. 2017. Deep learning for urban remote sensing. Joint Urban Remote Sensing Event (JURSE)IEEE, 1–4.
  • Audebert N, Le Saux B, Lefèvre S. 2017. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. Asian Conference on Computer Vision, 180–196.
  • Badrinarayanan V, Kendall A, Cipolla R. 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 39(12):2481–2495.
  • Bakhtiari HRR, Abdollahi A, Rezaeian H. 2017. Semi automatic road extraction from digital images. Egyptian J Remote Sens Space Sci. 20(1):117–123. http://www.sciencedirect.com/science/article/pii/S1110982317300820.
  • Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs. IEEE Trans Pattern Anal Mach Intell.
  • Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille ALJITOPA, Intelligence M. 2018. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell. 40(4):834–848.
  • Do N-T, Joo S-D, Yang H-J, Jung ST, Kim S-H. 2019. Knee bone tumor segmentation from radiographs using seg-unet with dice loss. 25th International Workshop on Frontiers of Computer Vision (IW-FCV), Gangneung, South Korea.
  • Farabet C, Couprie C, Najman L, Lecun Y. 2013. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell. 35(8):1915–1929.
  • Fu G, Liu C, Zhou R, Sun T, Zhang Q. 2017. Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing. 9(5):498.
  • Ghasemkhani N, Vayghan SS, Abdollahi A, Pradhan B, Alamri A. 2020. Urban development modeling using integrated fuzzy systems, ordered weighted averaging (owa), and geospatial techniques. Sustainability. 12(3):809.
  • Girshick R, Donahue J, Darrell T, Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; p. 580–587. https://arxiv.org/abs/1311.2524.
  • He K, Zhang X, Ren S, Sun J. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision; p. 1026–1034. https://arxiv.org/abs/1502.01852.
  • Hu F, Xia G-S, Hu J, Zhang L. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11):14680–14707.
  • Huertas A, Nevatia R. 1988. Detecting buildings in aerial images. Computer Vision, Graphics, Image Process. 41(2):131–152.
  • Inglada J. 2007. Automatic recognition of man-made objects in high resolution optical remote sensing images by svm classification of geometric image features. ISPRS J Photogramm Remote Sens. 62(3):236–248.
  • Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 1:1097–1105.
  • Kussul N, Shelestov A, Lavreniuk M, Butko I, Skakun S. 2016. Deep learning approach for large scale land cover mapping based on remote sensing data fusion. IEEE International Geoscience and Remote Sensing Symposium (IGARSS);p. 198–201.
  • Levitt S, Aghdasi F. 1998. An investigation into the use of wavelets and scaling for the extraction of buildings in aerial imagesed. Proceedings of the 1998 South African Symposium on Communications and Signal Processing-COMSIG'98 (Cat. No. 98EX214); p. 133–138.
  • Long J, Shelhamer E, Darrell T. 2016. Fully convolutional networks for semantic segmentationed. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; p. 3431–3440.
  • Maggiori E, Tarabalka Y, Charpiat G, Alliez P. 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens. 55(2):645–657.
  • Marcu A, Leordeanu M. 2016. Dual local-global contextual pathways for recognition in aerial imagery. https://arxiv.org/abs/1605.05462.
  • Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U. 2018. Classification with an edge: improving semantic image segmentation with boundary detection. ISPRS J Photogramm Remote Sens. 135:158–172.
  • Marmanis D, Wegner JD, Galliani S, Schindler K, Datcu M, Stilla U. 2016. Semantic segmentation of aerial images with an ensemble of cnss. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. III-3:473–480.
  • Mayer H. 1999. Automatic object extraction from aerial imagery—a survey focusing on buildings. Computer Vision Image Understanding. 74(2):138–149.
  • Mnih V. 2013. Machine learning for aerial image labeling [ph.D. Dissertation]. Department of Computer Science, University of Toronto, Canada.
  • Paisitkriangkrai S, Sherrah J, Janney P, Van Den Hengel A. 2016. Semantic labeling of aerial and satellite imagery. IEEE J Sel Top Appl Earth Observ Remote Sens. 9(7):2868–2881.
  • Penatti OA, Nogueira K, Dos Santos JA. 2015. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops;p. 44–51.
  • Peng J, Liu Y. 2005. Model and context‐driven building extraction in dense urban aerial images. Int J Remote Sens. 26(7):1289–1307.
  • Ronneberger O, Fischer P, Brox T. 2015. U-net: Convolutional networks for biomedical image segmentationed. International Conference on Medical Image Computing and Computer-Assisted Intervention; p. 234–241.
  • Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S. 2015. Cnn features off-the-shelf: an astounding baseline for recognitioned. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; p. 806–813.
  • Sherrah J. 2016. Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. https://arxiv.org/abs/1606.02585.
  • Shrestha S, Vanneschi L. 2018. Improved fully convolutional network with conditional random fields for building extraction. Remote Sens. 10(7):1135.
  • Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition; p. 1–14. https://arxiv.org/abs/1409.1556.
  • Sumer E, Turker M. 2013. An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images. Comput Environ Urban Syst. 39:48–62.
  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutionsed. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; p. 1–9.
  • Vakalopoulou M, Karantzalos K, Komodakis N, Paragios N. 2015. Building detection in very high resolution multispectral data with deep learning features. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE; p. 1873–1876.
  • Volpi M, Tuia D. 2017. Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans Geosci Remote Sens. 55(2):881–893.
  • Wang S, Hou X, Zhao X. 2020. Automatic building extraction from high-resolution aerial imagery via fully convolutional encoder-decoder network with non-local block. IEEE Access. 8:7313–7322.
  • Wilkinson GG. 2005. Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans Geosci Remote Sens. 43(3):433–440.
  • Yang X, Ye Y, Li X, Lau RY, Zhang X, Huang X. 2018. Hyperspectral image classification with deep learning models. IEEE Trans Geosci Remote Sens. 56(9):5408–5423.
  • Yuan J. 2018. Learning building extraction in aerial scenes with convolutional networks. IEEE Trans Pattern Anal Mach Intell. 40(11):2793–2798.
  • Yuan J, Cheriyadat AM. 2014. Learning to count buildings in diverse aerial scenes. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; p. 271–280.
  • Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A. 2014. Learning deep features for scene recognition using places database. 27th International Conference on Neural Information Processing Systems, 1; p. 487–495, Montreal, Canada.

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