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Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Remote sensing (Basel, Switzerland), 2018-01, Vol.10 (1), p.144 [Peer Reviewed Journal]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10010144

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  • Title:
    Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters
  • Author: Xu, Yongyang ; Wu, Liang ; Xie, Zhong ; Chen, Zhanlong
  • Subjects: building extraction ; deep learning ; guided filter ; very high resolution
  • Is Part Of: Remote sensing (Basel, Switzerland), 2018-01, Vol.10 (1), p.144
  • Description: Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.
  • Publisher: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs10010144
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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