A REAL-WORLD HYPERSPECTRAL IMAGE PROCESSING WORKFLOW FOR VEGETATION STRESS AND HYDROCARBON INDIRECT DETECTION

In this work, we present the complete workflow used to acquire a large hyperspectral dataset on a western Africa historical hydrocarbon production site, and its processing. Our goal is to study how state-of-the-art hyperspectral processing techniques can help detect hydrocarbon bearing soil either o...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2020-01, Vol.XLIII-B3-2020, p.395-400
Main Authors: Dubucq, D., Audebert, N., Achard, V., Alakian, A., Fabre, S., Credoz, A., Deliot, P., Le Saux, B.
Format: Article
Language:English
Subjects:
Atmospheric correction
Environmental monitoring
Historic buildings & sites
Historic sites
Hydrocarbons
Hyperspectral imaging
Image acquisition
Image annotation
Image classification
Image processing
Machine learning
Soil
State-of-the-art reviews
Vegetation
Workflow
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Description
Summary:In this work, we present the complete workflow used to acquire a large hyperspectral dataset on a western Africa historical hydrocarbon production site, and its processing. Our goal is to study how state-of-the-art hyperspectral processing techniques can help detect hydrocarbon bearing soil either of natural origin or accidental by monitoring the health of the vegetation for exploration or environmental monitoring purposes. We present our complete workflow, from acquisition, atmospheric correction, image annotation and classification using modern machine learning techniques, and show how state-of-the-art research can be applied to real-world use cases.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B3-2020-395-2020