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Land cover and soil type mapping from spaceborne PolSAR Data at L-Band with probabilistic neural network / Oleg Antropov in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 1 (September 2014) . - pp 5256 - 5270 Titre : Land cover and soil type mapping from spaceborne PolSAR Data at L-Band with probabilistic neural network Type de document : Article/Communication Auteurs : Oleg Antropov, Auteur ; Yrjö Rauste, Auteur ; Heikki Astola, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5256 - 5270 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification dirigée [Termes IGN] données polarimétriques [Termes IGN] forêt boréale [Termes IGN] image ALOS-PALSAR [Termes IGN] réseau neuronal artificiel [Termes IGN] sol Résumé : (Auteur) This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping. Numéro de notice : A2014-439 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287712 En ligne : https://doi.org/10.1109/TGRS.2013.2287712 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73976 [article]

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