Building extraction in very high resolution remote sensing imagery using deep learning and guided filters

Remote Sensing

Volumn 10, Issue 1, 2018, Pages

  Xu, Yongyang ^a   Wu, Liang ^a   Xie, Zhong ^a   Chen, Zhanlong ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

Building extraction; Deep learning; Guided filter; Very high resolution

Indexed keywords

BANDPASS FILTERS; BUILDINGS; EXTRACTION; IMAGE PROCESSING; IMAGE SEGMENTATION; LAND USE; LEARNING SYSTEMS; NETWORK ARCHITECTURE; PIXELS; REMOTE SENSING; SEMANTICS;

BUILDING EXTRACTION; GUIDED FILTERS; LAND COVER CLASSIFICATION; LANDUSE CLASSIFICATIONS; REMOTE SENSING IMAGERY; REMOTE SENSING IMAGES; SALT-AND-PEPPER NOISE; VERY HIGH RESOLUTION;

DEEP LEARNING;

EID: 85040839625     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10010144     Document Type: Article

References (52)

1. Moser, G.; Serpico, S.B.; Benediktsson, J.A. Land-Cover Mapping by Markov Modeling of Spatial-Contextual Information in Very-High-Resolution Remote Sensing Images. Proc. IEEE. 2013, 101, 631-651
2. Longbotham, N.; Chaapel, C.; Bleiler, L.; Padwick, C.; Emery, W.J.; Pacifici, F. Very High Resolution Multiangle Urban Classification Analysis. IEEE Trans. Geosci. Remote Sens. 2012, 50, 1155-1170
3. Mnih, V.; Hinton, G.E. Learning to Detect Roads in High-Resolution Aerial Images. In Proceedings of the Computer Vision-ECCV 2010-European Conference on Computer Vision, Heraklion, Crete, Greece, 5-11 September 2010; pp. 210-223
4. Dalponte, M.; Bruzzone, L.; Gianelle, D. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 2012, 123, 258-270
5. Lv, Z.Y.; He, H.; Benediktsson, J.A.; Huang, H. A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery. Remote Sens. 2016, 8, 814
6. Tian, S.; Zhang, X.; Tian, J.; Sun, Q. Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China. Remote Sens. 2016, 8, 954
7. Kumar, A.L.; Sinha, P.; Taylor, S. Improving image classification in a complex wetland ecosystem through image fusion techniques. J. Appl. Remote Sens. 2014, 8, 083616
8. Wang, J.; Song, J.; Chen, M.; Yang, Z. Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine. Int. J. Remote Sens. 2015, 36, 3144-3169
9. Konstantinidis, D.; Stathaki, T.; Argyriou, V.; Grammalidis, N. Building Detection Using Enhanced HOG-LBP Features and Region Refinement Processes. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 888-905
10. Kampffmeyer, M.; Salberg, A.B.; Jenssen, R. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks. In Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June-1 July 2016; pp. 680-688
11. Liu, F.; Jiao, L.; Hou, B.; Yang, S. POL-SAR Image Classification Based onWishart DBN and Local Spatial Information. IEEE Trans. Geosci. Remote Sens. 2016, 54, 3292-3308
12. Marmanis, D.; Schindler, K.; Wegner, J.D.; Galliani, S.; Datcu, M.; Stilla, U. Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection. arXiv 2016, arXiv:1612.01337
13. Zhang, Y. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS J. Photogramm. Remote Sens. 1999, 54, 50-60
14. Huang, X.; Zhang, L. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy. Int. J. Remote Sens. 2009, 30, 3205-3221
15. Lv, Z.Y.; Zhang, P.; Benediktsson, J.A.; Shi, W.Z. Morphological Profiles Based on Differently Shaped Structuring Elements for Classification of Images With Very High Spatial Resolution. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 7, 4644-4652
16. Wang, Y.; Song, H.; Zhang, Y. Spectral-spatial classification of hyperspectral images using joint bilateral filter and graph cut based model. Remote Sens. 2016, 8, 748
17. Razavian, A.S.; Azizpour, H.; Sullivan, J.; Carlsson, S. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 23-28 June 2014; pp. 512-519
18. Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. arXiv 2014, arXiv:1311.2524
19. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 3431-3440
20. Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv 2016, arXiv:1606.00915
21. Zheng, S.; Jayasumana, S.; Romera-Paredes, B.; Vineet, V.; Su, Z.; Du, D.; Huang, C.; Torr, P.H. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 7-13 December 2015; pp. 1529-1537
22. Audebert, N.; Boulch, A.; Lagrange, A.; Le Saux, B.; Lefevre, S. Deep Learning for Remote Sensing; Technical Report; ONERA The French Aerospace Lab, DTIM & Univ. Bretagne-Sud & ENSTA ParisTech: Palaiseau, France, 2016
23. Penatti, O.A.B.; Nogueira, K.; Santos, J.A.D. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA, 7-12 June 2015; pp. 44-51
24. Audebert, N.; Boulch, A.; Randrianarivo, H.; Le Saux, B.; Ferecatu, M.; Lefèvre, S.; Marlet, R. Deep learning for urban remote sensing. In Proceedings of the Urban Remote Sensing Event (JURSE), Dubai, UAE, 6-8 March 2017; pp. 1-4
25. Vakalopoulou, M.; Karantzalos, K.; Komodakis, N.; Paragios, N. Building detection in very high resolution multispectral data with deep learning features. In Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015; pp. 1873-1876
26. Farabet, C.; Couprie, C.; Najman, L.; Lecun, Y. Learning Hierarchical Features for Scene Labeling. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 35, 1915-1929
27. Wilkinson, G.G. Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans. Geosci. Remote Sens. 2005, 43, 433-440
28. Paisitkriangkrai, S.; Sherrah, J.; Janney, P.; Hengel, V.-D. Effective semantic pixel labelling with convolutional networks and conditional random fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA, 7-12 June 2015; pp. 36-43
29. Paisitkriangkrai, S.; Sherrah, J.; Janney, P.; Hengel, A.V.D. Semantic Labeling of Aerial and Satellite Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2868-2881
30. Zhong, Z.; Li, J.; Cui, W.; Jiang, H. Fully convolutional networks for building and road extraction: Preliminary results. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10-15 July 2016; pp. 1591-1594
31. Fu, G.; Liu, C.; Zhou, R.; Sun, T.; Zhang, Q. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens. 2017, 9, 498
32. Maggiori, E.; Tarabalka, Y.; Charpiat, G.; Alliez, P. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Trans. Geosci. Remote Sens. 2017, 55, 645-657
33. Audebert, N.; Saux, B.L.; Lefèvre, S. Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks. In Proceedings of the Computer Vision-ACCV 2016, Taipei, Taiwan, 20-24 November 2016; pp. 180-196
34. Dosovitskiy, A.; Fischer, P.; Ilg, E.; Hausser, P.; Hazirbas, C.; Golkov, V.; van der Smagt, P.; Cremers, D.; Brox, T. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 7-13 December 2015; pp. 2758-2766
35. Marmanis, D.; Wegner, J.D.; Galliani, S.; Schindler, K.; Datcu, M.; Stilla, U. Semantic Segmentation of Aerial Images with an Ensemble of CNSS. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 3, 473-480
36. Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv 2014, arXiv:1412.7062
37. Sherrah, J. Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery. arXiv 2016, arXiv:1606.02585
38. Dalla Mura, M.; Benediktsson, J.A.; Waske, B.; Bruzzone, L. Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3747-3762
39. Tokarczyk, P.; Wegner, J.D.; Walk, S.; Schindler, K. Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2014, 53, 280-295
40. Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; Schiele, B. The Cityscapes Dataset for Semantic Urban Scene Understanding. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27-30 June 2016; pp. 3213-3223
41. Volpi, M.; Tuia, D. Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks. IEEE Trans. Geosci. Remote Sens. 2016, 55, 881-893
42. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June-1 July 2016; pp. 770-778
43. Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 2015, arXiv:1409.1556
44. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. In Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 3-6 December 2012; pp. 1097-1105
45. Hu, F.; Xia, G.S.; Hu, J.; Zhang, L. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery. Remote Sens. 2015, 7, 14680-14707
46. Lagrange, A.; Saux, B.L.; Beaupère, A.; Boulch, A.; Chan-Hon-Tong, A.; Herbin, S.; Randrianarivo, H.; Ferecatu, M. Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks. In Proceedings of the Geoscience and Remote Sensing Symposium, Milan, Italy, 26-31 July 2015; pp. 4173-4176
47. Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5-9 October 2015; pp. 234-241
48. Lin, T.Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature Pyramid Networks for Object Detection. arXiv 2016, arXiv:1612.03144
49. He, K.; Sun, J.; Tang, X. Guided Image Filtering. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1397-1409
50. Pritika; Budhiraja, S. Multimodal medical image fusion using modified fusion rules and guided filter. In Proceedings of the International Conference on Computing, Communication & Automation, Noida, India, 15-16 May 2015; pp. 1067-1072
51. Gerke, M. Use of the Stair Vision Library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen); Technical Report; University of Twente: Enschede, The Netherlands, 2015
52. Wang, H.; Wang, Y.; Zhang, Q.; Xiang, S.; Pan, C. Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images. Remote Sens. 2017, 9, 446