Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation

• URL: http://arxiv.org/abs/2211.15429v1
• Date: Thu, 17 Nov 2022 17:36:05 GMT
• Title: Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
• Authors: Alexis Groshenry, Clement Giron, Thomas Lauvaux, Alexandre d'Aspremont, Thibaud Ehret
• Abstract summary: New generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m) We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas.
• Score: 67.32835203947133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas. To compensate for the relative scarcity of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally expensive synthetic plume generation from Large Eddy Simulations by generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).

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