Micrometric mapping of total organic carbon in lake sediment cores combining fusion of multiresolution hyperspectral images and PLSR analysis
Résumé
Sedimentary cores are used, thanks to their physical, chemical and biological properties, to infer past climate and environment. Sampling methods (millimetre or centimetre) and routine analysis are destructive and non-spatially resolved methods that consume time and material. The use of hyperspectral imaging makes it possible to have micrometric area in each point of the core. We use two hyperspectral cameras, the VNIR (spectral range: 400-1000nm, spatial resolution: 60μm) and the SWIR (spectral range: 1000-2500nm, spatial resolution: 189μm). Usually each camera is used separately. The goal of this work is to show the combination of sensor increase performance predictions. A pixel-level data fusion based on the ARSIS method [1] is applied to create a unique cube at the optimal resolution. This new cube can be used with a usual PLSR method to develop a model for the total organic carbon.Three cores from the lakes Le Bourget, Annecy and Geneva (Western Alps) are been tested (approximately 60cm long and 9cm width each). For both samples, the results show an increase prediction performance rather than data used separately. In the unique cube, the selected wavelength (chlorophyll area (nm), C-H bonds ( nm) and O-H bonds ( nm)) corresponds to those selected by each sensor. Although the analyzes were performed on bulk samples (5 mm x 90 mm x 45 mm slices), the prediction model provides access to the mapping of the surface with a micrometric resolution (the 60μm pixel can be interpreted as relevant information).