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Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 733 - 743 Titre : Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades Type de document : Article/Communication Auteurs : Caiyun Zhang, Auteur Année de publication : 2014 Article en page(s) : pp. 733 - 743 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] cartographie thématique [Termes IGN] données lidar [Termes IGN] Floride (Etats-Unis) [Termes IGN] image hyperspectrale [Termes IGN] marais [Termes IGN] semis de points [Termes IGN] traitement d'image [Termes IGN] végétation Résumé : (Auteur)This study explored a combination of hyperspectral and lidar systems for vegetation mapping in the Florida Everglades. A framework was designed to integrate two remotely sensed datasets and four data processing techniques. Lidar elevation and intensity features were extracted from the original point cloud data to avoid the errors and uncertainties in the raster-based lidar methods. Lidar significantly increased the classification accuracy compared with the application of hyperspectral data alone. Three lidar-derived features (elevation, intensity, and topography) had the same contributions in the classification. A synergy of hyperspectral imagery with all lidar-derived features achieved the best result with an overall accuracy of 86 percent and a Kappa value of 0.82 based on an ensemble analysis of three machine learning classifiers. Ensemble analysis did not significantly increase the classification accuracy, but it provided a complementary uncertainty map for the final classified map. The study shows the promise of the synergy of hyperspectral and lidar systems for mapping complex wetlands. Numéro de notice : A2014-344 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.733 En ligne : https://doi.org/10.14358/PERS.80.8.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73717 [article]