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- W4312373003 abstract "This study aims to demonstrate the capability of Machine Learning algorithms to retrieve enhanced (on top of atmospheric background) methane concentrations from space-borne hyperspectral images. As most ML algorithms rely on existing data, they face both suitable data availability issues and methane retrieval biases caused by residual noise and/or geometric distortions. These limitations frequently cause insufficiently trained algorithms in terms of generalization and can lead to a failure in the training process. Hence, we developed an image simulator, the Remote Sensing Image Simulation Environment (RISE), which produces user-defined simulated ground-truth reference images for different scenes and sensors. Consequently, RISE can be used to create training datasets for ML algorithms across a wide range of applications.. With RISE, realistic methane plume datasets occurring from point source leakages can be created and processed, retrieving intermediate products such as top-of-atmosphere (TOA) radiances, level 1 (L1) images as well as enhanced methane concentration maps. Enhanced methane concentration maps paired with L1 spectra were used to train various ML regression models. Preliminary results prove the capability of ML algorithms to quantify enhanced methane concentrations." @default.
- W4312373003 created "2023-01-04" @default.
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- W4312373003 date "2022-09-13" @default.
- W4312373003 modified "2023-10-18" @default.
- W4312373003 title "Deep Learning Methane Retrievals Based On Synthetic Data" @default.
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- W4312373003 doi "https://doi.org/10.1109/whispers56178.2022.9955123" @default.
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