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Comparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
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Titre : Comparative study on projected clustering methods for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 307 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] OrclusRésumé : (Auteur) In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction. Numéro de notice : A2016-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047416 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80385
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 296 - 307[article]Exemplaires(1)
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