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Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier / R. Philipps in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 64 n° 1 (January - February 2009) . - pp 107 - 116 Titre : Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier Type de document : Article/Communication Auteurs : R. Philipps, Auteur ; L. Watson, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 107 - 116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse en composantes principales [Termes IGN] classificateur paramétrique [Termes IGN] classification hybride [Termes IGN] décomposition d'image [Termes IGN] image Landsat-ETM+ [Termes IGN] image multitemporelle [Termes IGN] précision de la classification [Termes IGN] Rondonia (Brésil) [Termes IGN] Virginie (Etats-Unis) Résumé : (Auteur) Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This paper introduces a feature reduction method based on the singular value decomposition (SVD). This SVD-based feature reduction method reduces the storage and processing requirements of the SVD by utilizing a training dataset. This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondônia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/non-forest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVD-based feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondônia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVD-based feature reduction can produce statistically significantly better classifications than PCA. Copyright ISPRS Numéro de notice : A2009-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29660 [article]

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