메뉴 건너뛰기




Volumn 12, Issue 6, 2020, Pages

BRRNet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images

Author keywords

Building extraction; Building residual refine network; Convolutional neural network; High resolution; Remote sensing images

Indexed keywords

COMPLEX NETWORKS; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; EXTRACTION; FORECASTING; IMAGE PROCESSING; POPULATION STATISTICS; REMOTE SENSING; TEXTURES;

EID: 85082301030     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs12061050     Document Type: Article
Times cited : (189)

References (43)
  • 1
    • 0030917979 scopus 로고    scopus 로고
    • Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect
    • Lo, C.P.; Quattrochi, D.A.; Luvall, J.C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. Int. J. Remote Sens. 1997, 15, 287-304.
    • (1997) Int. J. Remote Sens , vol.15 , pp. 287-304
    • Lo, C.P.1    Quattrochi, D.A.2    Luvall, J.C.3
  • 2
    • 33646496591 scopus 로고    scopus 로고
    • Application of high-resolution stereo satellite images to detailed landslide hazard assessment
    • Nichol, J.E.; Shaker, A.; Wong, M.S. Application of high-resolution stereo satellite images to detailed landslide hazard assessment. Geomorphology 2006, 76, 68-75.
    • (2006) Geomorphology , vol.76 , pp. 68-75
    • Nichol, J.E.1    Shaker, A.2    Wong, M.S.3
  • 3
    • 15944411251 scopus 로고    scopus 로고
    • THE APPLICATION OF HIGH RESOLUTION SATELLITE REMOTELY SENSED DATA TO LANDUSE DYNAMIC MONITORING
    • Yang, Q.h.; Qi, J.w.; Sun, Y.j. THE APPLICATION OF HIGH RESOLUTION SATELLITE REMOTELY SENSED DATA TO LANDUSE DYNAMIC MONITORING. Remote Sens. Land Resour. 2001, 4.
    • (2001) Remote Sens. Land Resour , pp. 4
    • Yang, Q.H.1    Qi, J.W.2    Sun, Y.j.3
  • 4
    • 2942563890 scopus 로고    scopus 로고
    • High spatial resolution remotely sensed data for ecosystem characterization
    • Wulder, M.A.; Hall, R.J.; Coops, N.C.; Franklin, S.E. High spatial resolution remotely sensed data for ecosystem characterization. BioScience 2004, 54, 511-521.
    • (2004) BioScience , vol.54 , pp. 511-521
    • Wulder, M.A.1    Hall, R.J.2    Coops, N.C.3    Franklin, S.E.4
  • 5
    • 13944252759 scopus 로고    scopus 로고
    • Segmentation of high-resolution remotely sensed data-concepts, applications and problems
    • Schiewe, J. Segmentation of high-resolution remotely sensed data-concepts, applications and problems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2002, 34, 380-385.
    • (2002) Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci , vol.34 , pp. 380-385
    • Schiewe, J.1
  • 6
    • 0037382547 scopus 로고    scopus 로고
    • Automated analysis of ultra high resolution remote sensing data for biotope type mapping: New possibilities and challenges
    • Ehlers, M.; Gähler, M.; Janowsky, R. Automated analysis of ultra high resolution remote sensing data for biotope type mapping: New possibilities and challenges. ISPRS J. Photogramm. Remote Sens. 2003, 57, 315-326.
    • (2003) ISPRS J. Photogramm. Remote Sens , vol.57 , pp. 315-326
    • Ehlers, M.1    Gähler, M.2    Janowsky, R.3
  • 7
    • 84861161549 scopus 로고    scopus 로고
    • Very high-resolution remote sensing: Challenges and opportunities [point of view]
    • Benediktsson, J.A.; Chanussot, J.; Moon, W.M. Very high-resolution remote sensing: Challenges and opportunities [point of view]. Proc. IEEE 2012, 100, 1907-1910.
    • (2012) Proc. IEEE , vol.100 , pp. 1907-1910
    • Benediktsson, J.A.1    Chanussot, J.2    Moon, W.M.3
  • 8
    • 85042350113 scopus 로고    scopus 로고
    • A critical review of high and very high-resolution remote sensing approaches for detecting and mapping slums: Trends, challenges and emerging opportunities
    • Mahabir, R.; Croitoru, A.; Crooks, A.; Agouris, P.; Stefanidis, A. A critical review of high and very high-resolution remote sensing approaches for detecting and mapping slums: Trends, challenges and emerging opportunities. Urban Sci. 2018, 2, 8.
    • (2018) Urban Sci , vol.2 , pp. 8
    • Mahabir, R.1    Croitoru, A.2    Crooks, A.3    Agouris, P.4    Stefanidis, A.5
  • 9
    • 0032208694 scopus 로고    scopus 로고
    • Building Detection and Description from a Single Intensity Image
    • Lin, C.; Nevatia, R. Building Detection and Description from a Single Intensity Image. Comput. Vis. Image Underst. 1998, 72, 101-121.
    • (1998) Comput. Vis. Image Underst , vol.72 , pp. 101-121
    • Lin, C.1    Nevatia, R.2
  • 13
    • 34247346536 scopus 로고    scopus 로고
    • A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery
    • Zhang, L.; Huang, X.; Huang, B.; Li, P. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2950-2961.
    • (2006) IEEE Trans. Geosci. Remote Sens , vol.44 , pp. 2950-2961
    • Zhang, L.1    Huang, X.2    Huang, B.3    Li, P.4
  • 14
    • 84899670758 scopus 로고    scopus 로고
    • BASI: A new index to extract built-up areas from high-resolution remote sensing images by visual attention model
    • Shao, Z.; Tian, Y.; Shen, X. BASI: a new index to extract built-up areas from high-resolution remote sensing images by visual attention model. Remote sensing letters. 2014, Volume 5, 305-314.
    • (2014) Remote sensing letters , vol.5 , pp. 305-314
    • Shao, Z.1    Tian, Y.2    Shen, X.3
  • 15
    • 33745726893 scopus 로고    scopus 로고
    • Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform
    • Seoul, Korea, 25-29 July
    • Liu, Z.; Wang, J.; Liu, W. Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. In Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 25-29 July 2005; Volume 4, pp. 2250-2253.
    • (2005) Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium , vol.4 , pp. 2250-2253
    • Liu, Z.1    Wang, J.2    Liu, W.3
  • 16
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, Volume 25, 1097-1105.
    • (2012) Adv. Neural Inf. Process. Syst , vol.25 , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 17
    • 84925410541 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • arXiv:1409.1556
    • Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
    • (2014) ArXiv
    • Simonyan, K.1    Zisserman, A.2
  • 20
    • 85053519750 scopus 로고    scopus 로고
    • Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification
    • Lv, X.; Ming, D.; Chen, Y.; Wang, M. Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification. Int. J. Remote Sens. 2019, 40, 506-531.
    • (2019) Int. J. Remote Sens , vol.40 , pp. 506-531
    • Lv, X.1    Ming, D.2    Chen, Y.3    Wang, M.4
  • 21
    • 85068144209 scopus 로고    scopus 로고
    • Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation
    • Chen, Y.; Ming, D.; Lv, X. Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation. Earth Sci. Inform. 2019, 1-23.
    • (2019) Earth Sci. Inform , pp. 1-23
    • Chen, Y.1    Ming, D.2    Lv, X.3
  • 22
    • 85075099977 scopus 로고    scopus 로고
    • SO-CNN based urban functional zone fine division with VHR remote sensing image
    • Zhou, W.; Ming, D.; Lv, X.; Zhou, K.; Bao, H.; Hong, Z. SO-CNN based urban functional zone fine division with VHR remote sensing image. Remote Sens. Environ. 2020, 236, 111458.
    • (2020) Remote Sens. Environ , vol.236 , pp. 111458
    • Zhou, W.1    Ming, D.2    Lv, X.3    Zhou, K.4    Bao, H.5    Hong, Z.6
  • 23
    • 85058871481 scopus 로고    scopus 로고
    • A new method for region-based majority voting CNNs for very high resolution image classification
    • Lv, X.; Ming, D.; Lu, T.; Zhou, K.; Wang, M.; Bao, H. A new method for region-based majority voting CNNs for very high resolution image classification. Remote Sens. 2018, 10, 1946.
    • (2018) Remote Sens , vol.10 , pp. 1946
    • Lv, X.1    Ming, D.2    Lu, T.3    Zhou, K.4    Wang, M.5    Bao, H.6
  • 26
    • 85033697420 scopus 로고    scopus 로고
    • Segnet: A deep convolutional encoder-decoder architecture for image segmentation
    • Badrinarayanan, V.; Kendall, A.; Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 2481-2495.
    • (2017) IEEE Trans. Pattern Anal. Mach. Intell , vol.39 , pp. 2481-2495
    • Badrinarayanan, V.1    Kendall, A.2    Cipolla, R.3
  • 27
    • 84906489074 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • Springer: Berlin, Germany
    • Zeiler, M.D.; Fergus, R. Visualizing and understanding convolutional networks. In European Conference on Computer Vision; Springer: Berlin, Germany, 2014; pp. 818-833.
    • (2014) European Conference on Computer Vision; , pp. 818-833
    • Zeiler, M.D.1    Fergus, R.2
  • 29
    • 85042712042 scopus 로고    scopus 로고
    • Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
    • 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. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 40, 834-848.
    • (2017) IEEE Trans. Pattern Anal. Mach. Intell , vol.40 , pp. 834-848
    • Chen, L.C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 31
    • 84992121956 scopus 로고    scopus 로고
    • Convolutional neural networks for large-scale remote-sensing image classification
    • Maggiori, E.; Tarabalka, Y.; Charpiat, G.; Alliez, P. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans. Geosci. Remote Sens. 2016, 55, 645-657.
    • (2016) IEEE Trans. Geosci. Remote Sens , vol.55 , pp. 645-657
    • Maggiori, E.1    Tarabalka, Y.2    Charpiat, G.3    Alliez, P.4
  • 32
    • 85044181374 scopus 로고    scopus 로고
    • Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks
    • Wu, G.; Shao, X.; Guo, Z.; Chen, Q.; Yuan, W.; Shi, X.; Xu, Y.; Shibasaki, R. Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sens. 2018, 10, 407.
    • (2018) Remote Sens , vol.10 , pp. 407
    • Wu, G.1    Shao, X.2    Guo, Z.3    Chen, Q.4    Yuan, W.5    Shi, X.6    Xu, Y.7    Shibasaki, R.8
  • 34
    • 85011384474 scopus 로고    scopus 로고
    • Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery
    • arXiv:1606.02585
    • Sherrah, J. Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv 2016, arXiv:1606.02585.
    • (2016) ArXiv
    • Sherrah, J.1
  • 35
    • 85040839625 scopus 로고    scopus 로고
    • Building extraction in very high resolution remote sensing imagery using deep learning and guided filters
    • Xu, Y.; Wu, L.; Xie, Z.; Chen, Z. Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens. 2018, 10, 144.
    • (2018) Remote Sens , vol.10 , pp. 144
    • Xu, Y.1    Wu, L.2    Xie, Z.3    Chen, Z.4
  • 36
    • 85060452567 scopus 로고    scopus 로고
    • Rethinking atrous convolution for semantic image segmentation
    • arXiv:1706.05587
    • Chen, L.C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv 2017, arXiv:1706.05587.
    • (2017) ArXiv
    • Chen, L.C.1    Papandreou, G.2    Schroff, F.3    Adam, H.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.