Fusion of multiresolution hyperspectral and fluorescence images for the analysis of sediment cores
Résumé
For solids environmental samples, spectroscopic properties can be analyzed but their interpretation is difficult due to the lack of common referential. For the spectroscopic images, pixels are relatively spatially referenced but in most cases, each sensor has his own spatial resolution.The sample used in this work is the first 30cm of a sedimentary core from the Lake Le Bourget (Western Alps), characterized by a stratified area corresponding to last eutrophic conditions of the lake.The aim of this work is to combine four images, (1) two hyperspectral images (9x15cm²): VNIR (98 bands, pixel: 60µm) and SWIR (144 bands, pixel: 189µm), and (2) two fluorescence images (2x10cm²; sub-sample of the previous one) using excitation wavelengths of 266nm and 355nm (1024 bands each, pixel: 100µm). Each hyperspectral data can be resume with a structured grayscale image. With these, it is possible to calculate a micro-deformation model (digital image correlation) and registered them with the same spatial dimension. Applying ARSIS method [1], a pixel level data fusion model is created to fuse all the spectra in a unique spatial cube with the optimal resolution using wavelet spatial transform (decomposition in 4 images: details, vertical, horizontal and diagonal). The new cube can be used as a new instrument.The ARSIS method allows to create a correlation model between the wavelet functions for all the resolution images used. This correlation can be used to add spatial structures to the low spatial resolute data calculated with wavelet transform.