Centre de documentation
scientifique

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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods / Samuel Adelabu in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

Public [article]  inGeocarto international > vol 30 n° 7 - 8 (August - September 2015) . - pp 810 - 821 Titre : Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods Type de document : Article/Communication Auteurs : Samuel Adelabu, Auteur ; Onisimo Mutanga, Auteur ; Elhadi Adam, Auteur Année de publication : 2015 Article en page(s) : pp 810 - 821 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] défoliation [Termes IGN] image multibande [Termes IGN] image RapidEye [Termes IGN] méthode fiable Résumé : (Auteur) In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm. Numéro de notice : A2015-503 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.997303 Date de publication en ligne : 04/02/2015 En ligne : http://www.tandfonline.com/doi/abs/10.1080/10106049.2014.997303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77420 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2015041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)  

Public [article]  inGeocarto international > vol 30 n° 3 - 4 (March - April 2015) . - pp 457 - 471 Titre : Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland Type de document : Article/Communication Auteurs : Samuel Adelabu, Auteur ; Timothy Dube, Auteur Année de publication : 2015 Article en page(s) : pp 457 - 471 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique du sud (état) [Termes IGN] analyse diachronique [Termes IGN] Botswana [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données de terrain [Termes IGN] espèce végétale [Termes IGN] forêt [Termes IGN] image hyperspectrale [Termes IGN] image Quickbird [Termes IGN] rééchantillonnage [Termes IGN] réflectance végétale [Termes IGN] savane Résumé : (Auteur) With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n = 5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments. Numéro de notice : A2015-306 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.885589 Date de publication en ligne : 31/03/2014 En ligne : https://doi.org/10.1080/10106049.2014.885589 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76524 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible