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Deep Learning on Hyperspectral Data for Land Use and Vegetation MappingNormal access

Authors: N. Audebert, B. Le Saux, S. Lefevere, C. Taillandier and D. Dubucq
Event name: 79th EAGE Conference and Exhibition 2017 - Workshops
Session: WS01 Data Science for Geosciences
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701653
Organisations: EAGE
Language: English
Info: Abstract, PDF ( 86.19Kb )
Price: € 20

Summary:
Remote sensing technology is a remarkable tool to explore and to measure Earth’s surface features. Total and ONERA set up a collaborative partnership named New Advanced Observation Method Integration (NAOMI) that aims at adapting and developing new remote sensing techniques specifically targeted for hydrocarbons exploration and environmental protection. In this context, we integrate deep learning for classification of hyperspectral data. To detect different land uses and materials in aerial hyperspectral images, neural networks prove themselves to be very efficient tools, as they are able to learn discriminant features that help classification performance.


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