A real-world hyperspectral image processing workflow for vegetation stress and hydrocarbon indirect detection

Dominique Dubucq (1) , Nicolas Audebert (2, 3) , Véronique Achard (4) , Alexandre Alakian (3) , Sophie Fabre (4) , Anthony Credoz (1) , Philippe Deliot (4) , Bertrand Le Saux (3, 5)

1 TOTAL S.A.
2 CEDRIC - VERTIGO - CEDRIC. Données complexes, apprentissage et représentations
3 DTIS, ONERA, Université Paris Saclay [Palaiseau]
4 DOTA, ONERA, Université de Toulouse [Toulouse]
5 ESA - Agence Spatiale Européenne = European Space Agency

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Réseau de neurones [cs.NE]; Traitement des images [eess.IV]; Biodiversité et Ecologie

Dominique Dubucq, Nicolas Audebert, Véronique Achard, Alexandre Alakian, Sophie Fabre, et al.. A real-world hyperspectral image processing workflow for vegetation stress and hydrocarbon indirect detection. XXIV ISPRS Congress, Aug 2020, Nice, France. ⟨10.5194/isprs-archives-XLIII-B3-2020-395-2020⟩. ⟨hal-02924091⟩