Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F21%3A00547965" target="_blank" >RIV/67985939:_____/21:00547965 - isvavai.cz</a>
Result on the web
<a href="http://hdl.handle.net/11104/0324138" target="_blank" >http://hdl.handle.net/11104/0324138</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/rse2.194" target="_blank" >10.1002/rse2.194</a>
Alternative languages
Result language
angličtina
Original language name
Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10618 - Ecology
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing in Ecology and Conservation
ISSN
2056-3485
e-ISSN
2056-3485
Volume of the periodical
7
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
355-368
UT code for WoS article
000611728600001
EID of the result in the Scopus database
2-s2.0-85099971758