Estimation of atmospheric PSF parameters for hyperspectral imaging

Abstract

SUMMARYWe present an iterative approach to solve separable nonlinear least squares problems arising in the estimation of wavelength-dependent point spread function parameters for hyperspectral imaging. A variable projection Gauss-Newton method is used to solve the nonlinear least squares problem. An analysis shows that the Jacobian can be potentially very ill conditioned. To deal with this ill conditioning, we use a combination of subset selection and other regularization techniques. Experimental results related to hyperspectral point spread function parameter identification and star spectrum reconstruction illustrate the effectiveness of the resulting numerical scheme.

Dedication to Robert J. Plemmons

“…The contributions cover different aspects in the research field of matrices with applications. In the first paper, Berisha et al developed an iterative approach to solve separable nonlinear least squares problems arising in the estimation of wavelength‐dependent point spread function parameters for hyperspectral imaging. Experimental results related to hyperspectral point spread function parameter identification and star spectrum reconstruction illustrate the effectiveness of the proposed numerical scheme.…”