Upscaling of solar induced chlorophyll fluorescence from leaf to canopy using the DART model and a realistic 3D forest scene

Non peer reviewe

Physics-based satellite-derived bathymetry for nearshore coastal waters in North America

Accurate bathymetric information is fundamental to safe maritime navigation and infrastructure development in the coastal zone, but is expensive to acquire with traditional methods. Satellite-derived bathymetry (SDB) has the potential to produce bathymetric maps at dramatically reduced cost per unit area and physics-based radiative transfer model inversion methods have been developed for this purpose. This thesis demonstrates the potential of physics-based SDB in North American coastal waters. First the utility of Landsat-8 data for SDB in Canadian waters was demonstrated. Given the need for precise atmospheric correction (AC) for deriving robust ocean color products such as bathymetry, the performances of different AC algorithms were then evaluated to determine the most appropriate AC algorithm for deriving ocean colour products such as bathymetry. Subsequently, an approach to minimize AC error was demonstrated for SDB in a coastal environment in Florida Keys, USA. Finally, an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, was demonstrated. Based on the findings of this thesis, it was concluded that: (1) Landsat-8 data hold great promise for physics-based SDB in coastal environments, (2) the problem posed by imprecise AC can be minimized by assessing and quantifying bias as a function of environmental factors, and then removing that bias in the atmospherically corrected images, from which bathymetry is estimated, and (3) an ensemble approach to SDB can produce results that are very similar to those obtained with the best individual image, but can be used to reduce time spent on pre-screening and filtering of scenes

Learning Wavefront Coding for Extended Depth of Field Imaging

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging