Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Bayesian methods for remote coastal measurement using imaging spectroscopy

Not Accessible

Your library or personal account may give you access

Abstract

Recent and future imaging spectrometers in air and orbit portend a revolution in capability for coastal and inland water studies. Bayesian model inversion methods are a powerful tool for analyzing these data. Their flexible prior distributions provide distinct advantages for the water optics environment, including numerical stability of the inversion, the ability to incorporate ancillary data, and rigorous propagation of uncertainties in the parameter estimates. This talk highlights an Optimal Estimation (OE) methodology from recent work which demonstrates closed uncertainty accounting for coastal and inland waters.

© 2019 The Author(s)

PDF Article
More Like This
The Portable Remote Imaging Spectrometer (PRISM) Coastal Ocean Sensor

Pantazis Mouroulis, Byron E. Van Gorp, Robert O. Green, Michael Eastwood, Daniel W. Wilson, Brandon Richardson, and Heidi Dierssen
RM2E.5 Optical Remote Sensing of the Environment (ORS) 2012

Modelling of Errors and Uncertainties in Photoacoustic Tomography using a Bayesian Framework

Tanja Tarvainen, Teemu Sahlström, Jenni Tick, and Aki Pulkkinen
MW4D.2 Mathematics in Imaging (MATH) 2019

Hyperspectral Remote Sensing of the Coastal Environment

David D. Kohler, W. Paul Bissett, Robert G. Steward, Mubin Kadiwala, and Robert Banfield
JWA19 Fourier Transform Spectroscopy (FTS) 2007

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved