Error analysis and minimum bound method for atmospheric remote sensing

Error analysis and minimum bound method for atmospheric remote sensing

In this study, we present an error analysis for Tikhonov regularization in a semi-stochastic setting. The analysis is carried out in such a way that it can be applied to any kind of inverse problem in atmospheric remote sensing. A method for selecting the optimal regularization parameter relying on the minimization of an estimator of the bound of the error between the first iterate and the exac...

Iteratively regularized Gauss–Newton method for atmospheric remote sensing

In this paper we present an inversion algorithm for nonlinear ill-posed problems arising in atmospheric remote sensing. The proposed method is the iteratively regularized Gauss–Newton method. The dependence of the performance and behaviour of the algorithm on the choice of the regularization matrices and sequences of regularization parameters is studied by means of simulations. A method for imp...

An Estimation Error Bound for Pixelated Sensing

This paper considers the ubiquitous problem of estimating the state (e.g., position) of an object based on a series of noisy measurements. The standard approach is to formulate this problem as one of measuring the state (or a function of the state) corrupted by additive Gaussian noise. This model assumes both (i) the sensor provides a measurement of the true target (or, alternatively, a separat...

A new computational framework for atmospheric and surface remote sensing

A Bayesian data-analysis framework is described for atmospheric and surface retrievals from remotely-sensed hyper-spectral data. Some computational techniques are highlighted for improved accuracy in the forward physics model.

Minimum Distance Classification in Remote Sensing