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Auteur L. Su |
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Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)
[article]
Titre : Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis Type de document : Article/Communication Auteurs : L. Su, Auteur Année de publication : 2009 Article en page(s) : pp 407 - 413 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] carte de la végétation
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste marge
[Termes IGN] zone semi-arideRésumé : (Auteur) In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach. Copyright ISPRS Numéro de notice : A2009-297 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.02.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29927
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 4 (July - August 2009) . - pp 407 - 413[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09041 SL Revue Centre de documentation Revues en salle Disponible Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery / L. Su in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
[article]
Titre : Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery Type de document : Article/Communication Auteurs : L. Su, Auteur ; M. Chopping, Auteur ; A. Rango, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 299 - 311 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte de la végétation
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] désert
[Termes IGN] image Terra-MISR
[Termes IGN] Nouveau-Mexique (Etats-Unis)
[Termes IGN] prairie
[Termes IGN] zone aride
[Termes IGN] zone semi-arideRésumé : (Auteur) Accurately mapping community types is one of the main challenges for monitoring arid and semi-arid grasslands with remote sensing. The multi-angle approach has been proven useful for mapping vegetation types in desert grassland. The Multi-angle Imaging Spectro-Radiometer (MISR) provides 4 spectral bands and 9 angular reflectance. In this study, 44 classification experiments have been implemented to find the optimal combination of MISR multi-angular data to mine the information carried by MISR data as effectively as possible. These experiments show the following findings: 1) The combination of MISR's 4 spectral bands at nadir and red and near infrared bands in the C, B, and A cameras observing off-nadir can obtain the best vegetation type differentiation at the community level in New Mexico desert grasslands. 2) The k parameter at red band of Modified–Rahman–Pinty–Verstraete (MRPV) model and the structural scattering index (SSI) can bring useful additional information to land cover classification. The information carried by these two parameters, however, is less than that carried by surface anisotropy patterns described by the MRPV model and a linear semi-empirical kernel-driven bidirectional reflectance distribution function model, the RossThin–LiSparseMODIS (RTnLS) model. These experiments prove that: 1) multi-angular reflectance raise overall classification accuracy from 45.8% for nadir-only reflectance to 60.9%. 2) With surface anisotropy patterns derived from MRPV and RTnLS, an overall accuracy of 68.1% can be obtained when maximum likelihood algorithms are used. 3) Support Vector Machine (SVM) algorithms can raise the classification accuracy to 76.7%. This research shows that multi-angular reflectance, surface anisotropy patterns and SVM algorithms can improve desert vegetation type differentiation importantly. Copyright Elsevier Numéro de notice : A2007-056 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.05.023 En ligne : https://doi.org/10.1016/j.rse.2006.05.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28421
in Remote sensing of environment > vol 107 n° 1-2 (15 March 2007) . - pp 299 - 311[article]