東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Sensitivity analysis in remote sensing

Ustinov, Eugene A.

FindBook

Google Book

Amazon

博客來

Sensitivity analysis in remote sensing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sensitivity analysis in remote sensing/ by Eugene A. Ustinov.
作者:	Ustinov, Eugene A.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xiii, 128 p. :ill., digital ;24 cm.
內容註:	1. Introduction: Remote Sensing and Sensitivity Analysis -- 2. Sensitivity Analysis: Differential Calculus of Models -- 3. Three Approaches to Sensitivity Analysis of Models -- 4. Sensitivity Analysis of Analytic Models: Applications of Differential and Variational Calculus -- 5. Sensitivity Analysis of Analytic Models: Linearization and Adjoint Approaches -- 6. Sensitivity Analysis of Numerical Models -- 7. Sensitivity Analysis of Models with higher-Order Differential Equations -- 8. Applications of Sensitivity Analysis in Remote Sensing -- Appendix Operations with matrices and vectors -- Index.
Contained By:	Springer eBooks
標題:	Remote sensing. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-15841-9
ISBN:	9783319158419 (electronic bk.)

Sensitivity analysis in remote sensing
Ustinov, Eugene A.

Sensitivity analysis in remote sensing[electronic resource] /by Eugene A. Ustinov. - Cham :Springer International Publishing :2015. - xiii, 128 p. :ill., digital ;24 cm. - SpringerBriefs in earth sciences,2191-5369. - SpringerBriefs in earth sciences..

1. Introduction: Remote Sensing and Sensitivity Analysis -- 2. Sensitivity Analysis: Differential Calculus of Models -- 3. Three Approaches to Sensitivity Analysis of Models -- 4. Sensitivity Analysis of Analytic Models: Applications of Differential and Variational Calculus -- 5. Sensitivity Analysis of Analytic Models: Linearization and Adjoint Approaches -- 6. Sensitivity Analysis of Numerical Models -- 7. Sensitivity Analysis of Models with higher-Order Differential Equations -- 8. Applications of Sensitivity Analysis in Remote Sensing -- Appendix Operations with matrices and vectors -- Index.

This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remote sensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remote sensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inverse problems of remote sensing. They also play an important role already while designing the remote sensing experiment, where they are used to estimate the retrieval uncertainties of the geophysical parameters with given measurement errors of the instrument, thus providing means for formulations of corresponding requirements to the specific remote sensing instrument. If the quantitative models of geophysical objects can be formulated in an analytic form, then sensitivity analysis is reduced to differential calculus. But in most cases, the practical geophysical models used in remote sensing are based on numerical solutions of forward problems - differential equations with initial and/or boundary conditions. As a result, these models cannot be formulated in an analytic form and this is where the methods of SA become indispensable. This book is intended for a wide audience. The beginners in remote sensing could use it as a single source, covering key issues of SA, from general principles, through formulation of corresponding linearized and adjoint problems, to practical applications to uncertainty analysis and inverse problems in remote sensing. The experts, already active in the field, may find useful the alternative formulations of some key issues of SA, for example, use of individual observables, instead of a widespread use of the cumulative cost function. The book also contains an overview of author's matrix operator approach to formulation of adjoint problems for forward problems based on the higher-order partial differential equations. This approach still awaits its publication in the periodic literature and thus may be of interest to readership across all levels of expertise.

ISBN: 9783319158419 (electronic bk.)

Standard No.: 10.1007/978-3-319-15841-9doiSubjects--Topical Terms:

535394
Remote sensing.

LC Class. No.: G70.4

Dewey Class. No.: 621.3678

Sensitivity analysis in remote sensing
LDR:04379nmm m2200349 m 4500 001 2001649
003 DE-He213
005 20151113161952.0
006 m d
007 cr nn 008maaau
008 151215s2015 gw s 0 eng d
020 $a 9783319158419 (electronic bk.)
020 $a 9783319158402 (paper)
024 7 $a 10.1007/978-3-319-15841-9 $2 doi
035 $a 978-3-319-15841-9
040 $a GP $c GP
041 0 $a eng
050 4 $a G70.4
072 7 $a RB $2 bicssc
072 7 $a TQ $2 bicssc
072 7 $a SCI019000 $2 bisacsh
072 7 $a NAT011000 $2 bisacsh
082 0 4 $a 621.3678 $2 23
090 $a G70.4 $b .U88 2015
100 1 $a Ustinov, Eugene A. $3 2145439
245 1 0 $a Sensitivity analysis in remote sensing $h [electronic resource] / $c by Eugene A. Ustinov.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xiii, 128 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in earth sciences, $x 2191-5369
505 0 $a 1. Introduction: Remote Sensing and Sensitivity Analysis -- 2. Sensitivity Analysis: Differential Calculus of Models -- 3. Three Approaches to Sensitivity Analysis of Models -- 4. Sensitivity Analysis of Analytic Models: Applications of Differential and Variational Calculus -- 5. Sensitivity Analysis of Analytic Models: Linearization and Adjoint Approaches -- 6. Sensitivity Analysis of Numerical Models -- 7. Sensitivity Analysis of Models with higher-Order Differential Equations -- 8. Applications of Sensitivity Analysis in Remote Sensing -- Appendix Operations with matrices and vectors -- Index.
520 $a This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remote sensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remote sensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inverse problems of remote sensing. They also play an important role already while designing the remote sensing experiment, where they are used to estimate the retrieval uncertainties of the geophysical parameters with given measurement errors of the instrument, thus providing means for formulations of corresponding requirements to the specific remote sensing instrument. If the quantitative models of geophysical objects can be formulated in an analytic form, then sensitivity analysis is reduced to differential calculus. But in most cases, the practical geophysical models used in remote sensing are based on numerical solutions of forward problems - differential equations with initial and/or boundary conditions. As a result, these models cannot be formulated in an analytic form and this is where the methods of SA become indispensable. This book is intended for a wide audience. The beginners in remote sensing could use it as a single source, covering key issues of SA, from general principles, through formulation of corresponding linearized and adjoint problems, to practical applications to uncertainty analysis and inverse problems in remote sensing. The experts, already active in the field, may find useful the alternative formulations of some key issues of SA, for example, use of individual observables, instead of a widespread use of the cumulative cost function. The book also contains an overview of author's matrix operator approach to formulation of adjoint problems for forward problems based on the higher-order partial differential equations. This approach still awaits its publication in the periodic literature and thus may be of interest to readership across all levels of expertise.
650 0 $a Remote sensing. $3 535394
650 1 4 $a Earth Sciences. $3 642591
650 2 4 $a Earth System Sciences. $3 1566948
650 2 4 $a Mathematical Applications in the Physical Sciences. $3 1566152
650 2 4 $a Remote Sensing/Photogrammetry. $3 890882
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
650 2 4 $a Mathematical Methods in Physics. $3 890898
650 2 4 $a Signal, Image and Speech Processing. $3 891073
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in earth sciences. $3 1565946
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-15841-9
950 $a Earth and Environmental Science (Springer-11646)