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Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)  

Public [article]  inGeocarto international > vol 32 n° 7 (July 2017) . - pp 735 - 748 Titre : Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area Type de document : Article/Communication Auteurs : Mohamed Barakat A. Gibril, Auteur ; Suzana Bakar, Auteur ; Kouame Yao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 735 - 748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification pixellaire [Termes IGN] fusion d'images [Termes IGN] image Landsat-8 [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] Malaisie [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] surface cultivée [Termes IGN] utilisation du sol [Termes IGN] zone intertropicale Résumé : (Auteur) In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images. Numéro de notice : A2017-453 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170893 Date de publication en ligne : 15/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170893 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86373 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)  

Public [article]  inGeocarto international > vol 32 n° 7 (July 2017) . - pp 759 - 776 Titre : Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor Type de document : Article/Communication Auteurs : Abel Chemura, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2017 Article en page(s) : pp 759 - 776 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte agricole [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Coffea arabica [Termes IGN] cultures [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] précision de la classification [Termes IGN] rayonnement proche infrarouge Résumé : (Auteur) Coffee is a commodity of international trade significance, and its value chain can benefit from age-specific thematic maps. This study aimed to assess the potential of Landsat 8 OLI to develop these maps. Using field-collected samples with the random forest classifier, splitting coffee into three age classes (Scheme A) was compared with running the classification with one compound coffee class (Scheme B). Higher overall classification accuracy was obtained in Scheme B (90.3% for OLI and 86.8% for ETM+) than in Scheme A (86.2% for OLI and 81.0% for ETM+). The NIR band of OLI was the most important band in intra-class discrimination of coffee. Landsat 8 OLI mapped area closely matched farm records (R2 = 0.88) compared to that of Landsat 7 ETM+ (R2 = 0.78). It was concluded that Landsat 8 OLI data can be used to produce age-specific thematic maps in coffee production areas although disaggregating coffee classes reduces overall accuracy. Numéro de notice : A2017-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1178812 Date de publication en ligne : 03/05/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1178812 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86374 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017071	RAB	Revue	Centre de documentation	En réserve L003	Disponible