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Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images

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    0547965 - BÚ 2022 RIV GB eng J - Journal Article
    Kislov, D. E. - Korznikov, K. A. - Altman, Jan - Vozmishcheva, A. S. - Krestov, P. V.
    Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images.
    Remote Sensing in Ecology and Conservation. Roč. 7, č. 3 (2021), s. 355-368. E-ISSN 2056-3485
    R&D Projects: GA MŠMT(CZ) LTAUSA19137; GA ČR GJ20-05840Y
    Institutional support: RVO:67985939
    Keywords : deep learning * forest damage detection * vegetation recognition
    OECD category: Ecology
    Impact factor: 5.787, year: 2021
    Method of publishing: Open access

    We used satellite imagery of very high resolution in visual spectra represented as pansharpened images (RGB channels). When predicting forest damage, we obtained accuracies higher than 90% on test data for recognition of both windthrow areas and damaged trees impacted by bark beetles. A comparative analysis indicated that the DCNN-based approach outperforms traditional pixel-based classification methods (AdaBoost, random forest, support vector machine, quadratic discrimination) by at least several percentage points. DCNNs can learn a specific pattern of the area of interest and thus yield fewer false positive decisions than pixel-based algorithms. The ability of DCNNs to generalize makes them a good tool for delineating smooth and ill-defined boundaries of damaged forest areas, such as windthrow patches.
    Permanent Link: http://hdl.handle.net/11104/0324138

     
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    Altman RemSenEC.pdf09 MBPublisher’s postprintopen-access
     
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