KEYWORDS: multiple-point statistics, MPS simulation, remote sensing, satellite imagery, colorization, spectral enhancement, GPU-based topography point clouds completion methods
Recent satellite imagery is a vast improvement in terms of spatial, spectral and temporal resolution over archive imagery. My Ph.D. focuses on a statistical approach that reconstructs the spectrum of old satellite imagery to match quality of recent imagery.
Methodologically, I improve and develop new and efficient algorithms especially for pixel-based multiple-point statistics (MPS) simulations. My research develops MPS algorithms that are easier to configure and that benefit from new computing architecture, for CPUs with parallelization, multi-socket, vectorization and advance use of Xeon Phi, and for coprocessors such as GPU or FPGA.
My methods are applicable, for example, for spectral enhancement, also known as colorization, of old or incomplete satellite imagery. This process can be divided into two main topics: the disaggregation, that can be seen as a deconvolution, like reconstruction of color from gray; and extrapolation, like reconstructing near infrared from color.
After acquiring a strong foundation in mathematics and physics in “Classes Préparatoires aux Grandes Ecoles”, I completed 3 years of engineering at the Mining Engineering School of Alès, with specialization in computing (equivalent to a M.Sc.). My strong knowledge in computing and in particular in software optimization and parallelization were recognized through the Intel® Modern Code Developer Challenge 2015, where I ended Grand Prize Winner. Because of my interdisciplinary scientific fascinations and my quest for new challenges, I began my Ph.D. in earth science at the University of Lausanne in January 2016.
Nussbaumer, R., Mariethoz, G., Gravey, M., Gloaguen, E., & Holliger, K. (2018). Accelerating Sequential Gaussian Simulation with a constant path. Computers & Geosciences, 112, 121–132. https://doi.org/10.1016/j.cageo.2017.12.006
A Fast Fourier Transform approach for pixel-based Multi-Point Statistics, SpatialStat 2017, Lancaster, 06.07.17: pptx
Enhanced classification of multi-temporal satellite images for change detection, SpatialStat 2017, Lancaster, 06.07.17: pptx
Increasing the spectral resolution of satellite images, Geostat 2016, Valencia, 08.09.16: pptx
Gridless, pattern-driven point cloud completion and extension, EGU, Vienna, 19.04.16: pptx
University of Lausanne,
Institute of Earth Surface Dynamics (IDYST),
Geopolis, Quartier Mouline
1015 Lausanne, Switzerland