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Joint Deep Learning for land cover and land use classification

Zhang, Ce, Sargent, Isabel, Pan, Xin, Li, Huapeng, Gardiner, Andy, Hare, Jonathon, Atkinson, Peter M.·2019·Remote Sensing of Environment
DOI 10.1016/j.rse.2018.11.014
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  • There are two general strategies for deep-learning-based land cover classification: object-oriented approaches based on DCNNs and semantic segmentation approaches based on end-to-end DCNNs.

    “There are two general strategies for deep-learning-based land cover classification: object-oriented approaches based on DCNNs and semantic segmentation approaches based on end-to-end DCNNs.”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Zhang et al., 2019

    Section 2.3 describes an object-based CNN, but uses it for land-use rather than land-cover classification. Section 4.2 also discusses fully convolutional networks for semantic segmentation. The paper does not present these as the two general strategies for deep-learning-based land-cover classification. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Compared with conventional land cover classification methods, deep models can accurately characterize complex contextual information contained in high-resolution images.

    “Thus, compared with conventional land cover classification methods, deep models can accurately characterize complex contextual information contained in high-resolution images (Tong et al., 2020, Huang et al., 2018a, Zhang et al., 2019, Srivastava et al., 2019, Zhong et al., 2020).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Zhang et al., 2019

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Research works printed but not linked: 53

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Metadata

Authors
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M.
ORCID
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2019-02
Volume
221
Pages
173-187
ISSN
0034-4257
DOI
10.1016/j.rse.2018.11.014
Cited by
451
References
3
Language
en
Licence
Funders
  • Ordnance Survey
  • Lancaster University (EAA7369)

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