Iterative regularization methods for atmospheric remote sensing

被引：23

作者：

Doicu, A ^{[1
]}

Schreier, F ^{[1
]}

Hess, M ^{[1
]}

机构：

[1] DLR German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany

来源：

JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER | 2004年 / 83卷 / 01期

关键词：

inverse problems; nonlinear least squares; regularization; atmospheric spectroscopy; remote sensing;

D O I：

10.1016/S0022-4073(02)00292-3

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper we present different inversion algorithms for nonlinear ill-posed problems arising in atmosphere remote sensing. The proposed methods are Landweber's method (LwM), the iteratively regularized Gauss-Newton method, and the conventional and regularizing Levenberg-Marquardt method. In addition, some accelerated LwMs and a technique for smoothing the Levenberg-Marquardt solution are proposed. The numerical performance of the methods is studied by means of simulations. Results are presented for an inverse problem in atmospheric remote sensing, i.e., temperature sounding with an airborne uplooking high-resolution far-infrared spectrometer. (C) 2003 Elsevier Ltd. All rights reserved.

引用

页码：47 / 61

页数：15

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