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University of North Texas (UNT) (2006)

Hyperspectral and Multispectral Image Analysis for Vegetation Study in the Greenbelt Corridor near Denton, Texas

Bhattacharjee, Nilanjana

Titre : Hyperspectral and Multispectral Image Analysis for Vegetation Study in the Greenbelt Corridor near Denton, Texas

Auteur : Bhattacharjee, Nilanjana

Université de soutenance : University of North Texas (UNT)

Grade : Master of Science 2006

Résumé
In this research, hyperspectral and multispectral images were utilized for vegetation studies in the greenbelt corridor near Denton. EO-1 Hyperion was the hyperspectral image and Landsat Thematic Mapper (TM) was the multispectral image used for this research. In the first part of the research, both the images were classified for land cover mapping (after necessary atmospheric correction and geometric registration) using supervised classification method with maximum likelihood algorithm and accuracy of the classification was also assessed for comparison. Hyperspectral image was preprocessed for classification through principal component analysis (PCA), segmented principal component analysis and minimum noise fraction (MNF) transform. Three different images were achieved after these pre-processing of the hyperspectral image. Therefore, a total of four images were classified and assessed the accuracy. In the second part, a more precise and improved land cover study was done on hyperspectral image using linear spectral unmixing method Finally, several vegetation constituents like chlorophyll a, chlorophyll b, caroteoids were distinguished from the hyperspectral image using feature-oriented principal component analysis (FOPCA) method and which component dominates which type of land cover particularly vegetation were correlated

Mots-clés : classification | hyperspectral | multispectral | principal component analysis |

Présentation

Page publiée le 23 avril 2011, mise à jour le 20 décembre 2018