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Communication Dans Un Congrès Année : 2007

Vine variety discrimination with airborne imaging spectroscopy

Résumé

We aim at the discrimination of varieties within a single plant species (Vitis vinifera) by means of airborne hyperspectral imagery collected using a CASI-2 sensor and supervised classification, both under constant and varying within-scene illumination conditions. Varying illumination due to atmospheric conditions (such as clouds) and shadows cause different pixels belonging to the same class to present different spectral vectors, increasing the within class variability and hindering classification. This is specially serious in precision applications such as variety discrimination in precision agriculture, which depends on subtle spectral differences. In this study, we use machine learning techniques for supervised classification, and we also analyze the variability within and among plots and within and among sites, in order to address the generalizability of the results.
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Dates et versions

hal-00321875 , version 1 (16-09-2008)

Identifiants

Citer

Marcos Ferreiro-Arman, Jose Luis Alba Castro, Saeid Homayouni, Jean-Pierre da Costa, Julio Martin-Herrero. Vine variety discrimination with airborne imaging spectroscopy. Remote Sensing and Modeling of Ecosystems for Sustainability IV, Aug 2007, San Diego, United States. pp.667909, ⟨10.1117/12.734177⟩. ⟨hal-00321875⟩
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