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Mathematical principles of remote sensing : making inferences from noisy data / Andrew S. Milman.
Author
Milman, Andrew S.
[Browse]
Format
Book
Language
English
Published/Created
Chelsea, Mich. : Sleeping Bear Press, 1999.
Description
1 online resource (xiii, 406 pages) : illustrations
Availability
Available Online
Taylor & Francis eBooks Complete
SCI-TECHnetBASE
Taylor & Francis eBooks Complete
Details
Subject(s)
Remote sensing
—
Mathematical models
[Browse]
Summary note
Mathematical Principles of Remote Sensing is an informative reference, or working textbook, on the mathematics, and general physical and chemical processes behind remote sensor measurements. The issues and mathematical principles important to remote sensing and data analysis are covered extensively, including measurements and noise, physics of electromagnetic radiation, and radiation transfer. Specific mathematical methods include covariance and probability analysis, regression, linear algebra, Fourier transforms, convolution, and others. This book is an essential reference for remote sensing scientists and engineers concerned with applications in radiation transfer, image processing, atmospheric and noise correction, and modelling.
Notes
Bibliographic Level Mode of Issuance: Monograph
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Language note
English
Contents
chapter 1 2A matrix equation
chapter 1 4.2 Mean values
chapter 1 5.2 Pieces of information
chapter (16)
chapter 1 8 Nonlinear systems
chapter (29)
chapter 2 1.2 Microwave radiation
chapter (37)
chapter 2 5 Line broadening
chapter (13)
chapter (17)
chapter 5 2.1 Vector and matrix transpose
chapter 5 2.3 Rank
chapter (39)
chapter UU=UU=I.
chapter 5 2.7 Determinants
chapter (57)
chapter 5 3.1 Trace
chapter 5 4.1 Nonsymmetric matrices
chapter 5 5 Singular value decomposition
chapter D=diag(s, s , ···, s ),
chapter 5 7 Non-negative definite matrices
chapter 5 8 Noise and the covariance matrix
chapter (143)
chapter 5 10 Finding eigenvectors
chapter (14)
chapter (20)
chapter (21)
chapter (42)
chapter (51)
chapter (62)
chapter (69)
chapter (74)
chapter (81)
chapter (85)
chapter (91)
chapter (96)
chapter (105)
chapter (109)
chapter 8 1d-Functions
chapter (31)
chapter 8 2.7 Some useful functions
chapter 8 3 Fourier series
chapter (55)
chapter 8 4 Discrete Fourier transform
chapter (86)
chapter (113)
chapter 8 7.3Z-transform
chapter References
chapter C (t)=E[ƒ*(x)ƒ(x+t )].
chapter (10)
chapter (24)
chapter (31) P(? )= F(? ) , (32) (33) (34) (35) (36) P(? )=F*(? )G(? ).
chapter D(t )=E[f
chapter 9 6 Separating waves from noiselike features
chapter (77)
chapter (82)
chapter (94)
chapter (30)
chapter (34)
chapter (38)
chapter h(x)=0.
chapter (47)
chapter 10 7.1 Symmetric kernel
chapter 10 7.2 Asymmetric kernel
chapter (63)
chapter w=0.02 w=0.05 w=0.10 j ?j vj ?j vj ?j vj
chapter y=f(x), (6)
chapter u=y'+(1-a)u.
chapter 11 2.1 Logistic equation
chapter (26)
chapter 11 3.1 Outline of the iteration process
chapter (I -?AA)A =A
chapter =(aI+AA) A(aI+AA )(aI+AA)
chapter 11 4.2 Truncated iteration
chapter (73)
chapter (78)
chapter (83)
chapter (104)
chapter 0 < 1-aKK <1.
chapter (131)
chapter (138)
chapter 12 1 Matrix approach
chapter 12 1.3 Effects of noise
chapter (32)
chapter (61)
chapter (68)
chapter (75)
chapter 12 3 Discussion
chapter 13 1.1 Spatial filtering
chapter 13 3,1 Effects of noise
chapter (27) 13.4 A Backus -Gilbert approach
chapter 13 6 An integral-equation approach
chapter (65)
chapter (70)
chapter 14 1.1 An application of Lagrange multipliers
chapter (27)
chapter (52)
chapter (60)
chapter 14 5 Inequalities
chapter (85) 14.7 Law of large numbers and the central limit theorem
chapter (90)
chapter (a, f, g)=a(f, g) and (f, a, g)=a(f, g).
chapter (111)
chapter (114)
chapter 14 9 Orthogonal expansions
chapter 14 10 Orthonormalization
chapter (133)
chapter =f(y-cy-dy) +f(y-cy-dy) +f(y-cy-dy)
chapter b =a t . (143)
chapter 14 13 Proof by induction.
Show 108 more Contents items
ISBN
1-135-45760-3
0-429-21997-0
0-203-30578-7
9786610290420
1-280-29042-0
OCLC
1000441262
1027145524
Doi
10.1201/b12790
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Mathematical principles of remote sensing : making inferences from noisy data / Andrew S. Milman.
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