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Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 1 [01/01/2022] . - pp 142 - 162 Titre : Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image Type de document : Article/Communication Auteurs : Efosa Gbenga Adagbasa, Auteur ; Samuel Adelabu, Auteur ; Tom W. Okello, Auteur Année de publication : 2022 Article en page(s) : pp 142 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par Perceptron multicouche [Termes IGN] distribution spatiale [Termes IGN] espèce végétale [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] MNS ASTER [Termes IGN] montagne [Termes IGN] Poaceae Résumé : (auteur) Understanding the spatial distribution of vegetation species is essential to gain knowledge on the recovery process of an ecosystem. Few studies have used deep learning and machine learning models for image processing focusing on forest/crop classification. This study, therefore, makes use of a multi-layer perceptron (MLP) deep neural network to discriminate grass species in a mountainous region using Sentinel-2 images. Vegetation indices, Sentinel-1 and ASTER DEM were combined with Sentinel-2 images to improve classification accuracy. Stratified K-fold was used to ensure balanced training and test data. The results, when compared with other commonly used machine learning models, outperformed them all. It produced a better discriminate of the grass species when ASTER DEM was combined with Sentinel-2 images, with overall F1 score of 92%. The results of the species discrimination show a general increase in increaser II species such as Eragrostis curvula and a decrease in decreaser species like Phragmites australis. Numéro de notice : A2022-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1704070 En ligne : https://doi.org/10.1080/10106049.2019.1704070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100378 [article]

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