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Integration of multitemporal/polarization C-band SAR data sets for land-cover classification / N. Park in International Journal of Remote Sensing IJRS, vol 29 n° 15-16 (August 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n° 15-16 (August 2008) . - pp 4667 - 4688 Titre : Integration of multitemporal/polarization C-band SAR data sets for land-cover classification Type de document : Article/Communication Auteurs : N. Park, Auteur ; K.H. Chi, Auteur Année de publication : 2008 Article en page(s) : pp 4667 - 4688 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse en composantes principales [Termes IGN] angle d'incidence [Termes IGN] bande C [Termes IGN] carte agricole [Termes IGN] image Envisat-ASAR [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] intégration de données [Termes IGN] occupation du sol [Termes IGN] polarimétrie radar Résumé : (Auteur) This paper investigates the potential of multitemporal/polarization C-band SAR data for land-cover classification. Multitemporal Radarsat-1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land-cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis-based approach and principal-component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal-component loading. The tau model applied as a decision-level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA-based features showed improved classification accuracy for both Radarsat-1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis-based approach. The integration of PCA-based features based on multiple polarization (i.e. HH from Radarsat-1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat-1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land-cover classes through more experiments. Numéro de notice : A2008-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160801947341 En ligne : https://doi.org/10.1080/01431160801947341 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29600 [article]