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Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 38 - 53 Titre : Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features Type de document : Article/Communication Auteurs : Peijun Du, Auteur ; Alim Samat, Auteur ; Björn Waske, Auteur ; Sicong Liu, Auteur ; Zhenhong Li, Auteur Année de publication : 2015 Article en page(s) : pp 38 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données polarimétriques [Termes IGN] image Radarsat [Termes IGN] polarimétrie radar [Termes IGN] Rotation Forest classification [Termes IGN] texture d'image Résumé : (auteur) Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. Numéro de notice : A2015-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78342 [article]