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Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°5-6 (March 2007) . - pp 963 - 984 Titre : Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data Type de document : Article/Communication Auteurs : S. Lu, Auteur ; K. Oki, Auteur Année de publication : 2007 Article en page(s) : pp 963 - 984 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture [Termes IGN] analyse comparative [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification [Termes IGN] surface cultivée [Termes IGN] Tokyo (Japon) [Termes IGN] utilisation du sol Résumé : (Auteur) Airborne hyperspectral remote sensing was applied to agricultural land in the Miura Peninsula, near the metropolis of Tokyo in Japan. The study area is characterized by complicated land use patches, which is the general characteristic of most agricultural lands in Japan. Several feature extraction/classification methods were examined in classifying the land use and plant species. The results showed that decision boundary feature extraction (DBFE) was better than principal component analysis (PCA) as the feature extraction method. Moreover, the pre-classification process using NDVI that separates the whole study area into vegetated area and non-vegetated areas also improved the classification accuracy. After the pre-procedures, the land use and plant species were finally mapped by maximum likelihood classification (MLC) or extraction and classification of homogeneous objects (ECHO). The best kappa (overall accuracy) of classification was 0.914 (92.4%) and 0.924 (93.3%) for MLC and ECHO, respectively. The best accuracies of each category for the image were 79.5% to 100% for plant species (watermelon, pumpkin, marigold, grass and tree), 88.7% to 100% for soil types, 97.8% for concrete, and 99.4% for vinyl-mulches. Although, built-up area has low estimation accuracy, this did not affect the overall classification accuracy because it covers only a very small area. Copyright Taylor & Francis Numéro de notice : A2007-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600771561 En ligne : https://doi.org/10.1080/01431160600771561 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28462 [article]

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The applications of holography / H.J. Caulfield (1970)

Public Titre : The applications of holography Type de document : Monographie Auteurs : H.J. Caulfield, Auteur ; S. Lu, Auteur Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 1970 Importance : 138 p. Format : 15 x 23 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Optique [Termes IGN] holographie Numéro de notice : 47208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=58624

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47208-01	25.00	Livre	Centre de documentation	En réserve M-103	Disponible