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Land-cover classification with high-resolution remote sensing images using transferable deep models

Tong, Xin-Yi, Xia, Gui-Song, Lu, Qikai, Shen, Huanfeng, Li, Shengyang, You, Shucheng, Zhang, Liangpei·2020·Remote Sensing of Environment
DOI 10.1016/j.rse.2019.111322
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  • Current pseudo-labeling approaches empirically choose a confidence threshold or set a fixed proportion for sample collection, which is hard to guarantee the accuracy of pseudo-labels assigned by the prediction model to its unknown domain.

    “The current approaches are empirically choosing a confidence threshold (Xu et al., 2020, Peng et al., 2021) or setting a fixed proportion for sample collection (Tong et al., 2020, Zhang et al., 2021a, Yan et al., 2019, Wang et al., 2022), which is hard to guarantee the accuracy of pseudo-labels assigned by the prediction model to its unknown domain.”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Tong et al., 2020

    Section 3.2.2 says the method selects the top µ target samples in each category and acknowledges that pseudo-labels may be inaccurate. But µ is a fixed number, not a fixed proportion, and the method additionally checks pseudo-labels against labeled source samples before using them. The copy provides no page number for these passages. (LLM, conf=0.88)

    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 Tong et al., 2020

  • Diverse imaging conditions lead to shifts in feature distributions of ground objects, which causes the classification method optimal for the source domain to drop drastically in performance on the target domain.

    “The attendant problem is that diverse imaging conditions will lead to shifts in feature distributions of ground objects, which causes the classification method optimal for certain annotated images (referred to as the source domain) to drop drastically in performance on newly acquired images (referred to as the target domain) (Tuia et al., 2016, Tong et al., 2020).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Tong et al., 2020

  • Five-Billion-Pixels extends the land cover dataset GID.

    “we propose the Five-Billion-Pixels dataset, which extends the land cover dataset GID (Tong et al., 2020, Yang et al., 2022).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Tong et al., 2020

  • Even if a practicable deep model is already trained on a well-annotated dataset, it may not be valid for other geographical areas or sensors because of feature distribution shifts between source and target domains.

    “Even if a practicable deep model is already trained on a well-annotated dataset, it may not be valid for other geographical areas or sensors because of the feature distribution shifts between the source and target domains (Tuia et al., 2016, Tong et al., 2020, Huang et al., 2023).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Tong et al., 2020

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Metadata

Authors
Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2020-02
Volume
237
Pages
111322
ISSN
0034-4257
DOI
10.1016/j.rse.2019.111322
Cited by
978
References
8
Language
en
Licence
Funders
  • Chinese Academy of Sciences (LSU-SJLY-2017-01)
  • Outstanding Youth Foundation of Jiangsu Province of China (2017CFA037)
  • National Natural Science Foundation of China (61922065, 61771350, 41820104006, 61871299)
  • Chinese Academy of Agricultural Sciences

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