Weakly supervised learning for treeline ecotone classification based on aerial orthoimages and an ancillary DSM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10448917" target="_blank" >RIV/00216208:11310/22:10448917 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022" target="_blank" >https://doi.org/10.5194/isprs-annals-V-3-2022-33-2022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/isprs-annals-V-3-2022-33-2022" target="_blank" >10.5194/isprs-annals-V-3-2022-33-2022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Weakly supervised learning for treeline ecotone classification based on aerial orthoimages and an ancillary DSM

Popis výsledku v původním jazyce

Convolutional neural networks (CNNs) effectively classify standard datasets in remote sensing (RS). Yet, real-world data are more difficult to classify using CNNs because these networks require relatively large amounts of training data. To reduce training data requirements, two approaches can be followed - either pretraining models on larger datasets or augmenting the available training data. However, these commonly used strategies do not fully resolve the lack of training data for land cover classification in RS. Our goal is to classify trees and shrubs from aerial orthoimages in the treeline ecotone of the Krkonoše Mountains, Czechia. Instead of training a model on a smaller, human-labelled dataset, we semiautomatically created training data using an ancillary normalised Digital Surface Model (nDSM) and image spectral information. This approach can complement existing techniques, trading accuracy for a larger labelled dataset while assuming that the classifier can handle the training data noise. Weakly supervised learning on a CNN led to 68.99% mean Intersection over Union ( IoU) and 81.65% mean F1-score for U-Net and 72.94% IoU and 84.35% mean F1-score for our modified U-Net on a test set comprising over 1000 manually labelled points. Notwithstanding the bias resulting from the noise in training data (especially in the least occurring tree class), our data show that standard semantic segmentation networks can be used for weakly supervised learning for local-scale land cover mapping.

Název v anglickém jazyce

Weakly supervised learning for treeline ecotone classification based on aerial orthoimages and an ancillary DSM

Popis výsledku anglicky

Convolutional neural networks (CNNs) effectively classify standard datasets in remote sensing (RS). Yet, real-world data are more difficult to classify using CNNs because these networks require relatively large amounts of training data. To reduce training data requirements, two approaches can be followed - either pretraining models on larger datasets or augmenting the available training data. However, these commonly used strategies do not fully resolve the lack of training data for land cover classification in RS. Our goal is to classify trees and shrubs from aerial orthoimages in the treeline ecotone of the Krkonoše Mountains, Czechia. Instead of training a model on a smaller, human-labelled dataset, we semiautomatically created training data using an ancillary normalised Digital Surface Model (nDSM) and image spectral information. This approach can complement existing techniques, trading accuracy for a larger labelled dataset while assuming that the classifier can handle the training data noise. Weakly supervised learning on a CNN led to 68.99% mean Intersection over Union ( IoU) and 81.65% mean F1-score for U-Net and 72.94% IoU and 84.35% mean F1-score for our modified U-Net on a test set comprising over 1000 manually labelled points. Notwithstanding the bias resulting from the noise in training data (especially in the least occurring tree class), our data show that standard semantic segmentation networks can be used for weakly supervised learning for local-scale land cover mapping.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

XXIV ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III

ISBN

—

ISSN

2194-9042

e-ISSN

2194-9050

Počet stran výsledku

6

Strana od-do

33-38

Název nakladatele

Copernicus Gesellschaft MBH

Místo vydání

Gottingen

Místo konání akce

Nice

Datum konání akce

6. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000855203200006