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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)  

Public [article]  inRemote sensing of environment > vol 107 n° 1-2 (15 March 2007) . - pp 299 - 311 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-aride Résumé : (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 [article]

Intercalibration of vegetation indices from different sensor systems / M.D. Steven in Remote sensing of environment, vol 88 n° 4 (30/12/2003)  

Public [article]  inRemote sensing of environment > vol 88 n° 4 (30/12/2003) . - pp 412 - 422 Titre : Intercalibration of vegetation indices from different sensor systems Type de document : Article/Communication Auteurs : M.D. Steven, Auteur ; T.J. Malthus, Auteur ; F. Baret, Auteur ; H. Xu, Auteur ; M. Chopping, Auteur Année de publication : 2003 Article en page(s) : pp 412 - 422 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données multicapteurs [Termes IGN] indice de végétation [Termes IGN] luminance lumineuse [Termes IGN] réflectance végétale [Termes IGN] simulation d'étalonnage Résumé : (Auteur) Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 12%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change. Numéro de notice : A2003-367 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.08.010 En ligne : https://doi.org/10.1016/j.rse.2003.08.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26447 [article]