The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 395–400, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-395-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 395–400, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-395-2020

  21 Aug 2020

21 Aug 2020

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

D. Dubucq1, N. Audebert2, V. Achard2, A. Alakian2, S. Fabre2, A. Credoz1, P. Deliot2, and B. Le Saux2 D. Dubucq et al.
  • 1TOTAL SA, 64018 PAU, France
  • 2ONERA, Toulouse & Saclay, France

Keywords: hyperspectral image processing, machine learning, unmixing, hydrocarbon indirect detection

Abstract. 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.